Reviewerly/exHarmony huggingface dataset:
Top-N Recommendation with Multi-Channel Positive Feedback using Factorization Machines

User interactions can be considered to constitute different feedback channels, for example, view, click, like or follow, that provide implicit information on users’ preferences. Each implicit feedback channel typically carries a unary, positive-only signal that can be exploited by collaborative filtering models to generate lists of personalized recommendations. This article investigates how a learning-to-rank recommender system can best take advantage of implicit feedback signals from multiple channels. We focus on Factorization Machines (FMs) with Bayesian Personalized Ranking (BPR), a pairwise learning-to-rank method, that allows us to experiment with different forms of exploitation. We perform extensive experiments on three datasets with multiple types of feedback to arrive at a series of insights. We compare conventional, direct integration of feedback types with our proposed method, which exploits multiple feedback channels during the sampling process of training. We refer to our method as multi-channel sampling. Our results show that multi-channel sampling outperforms conventional integration, and that sampling with the relative “level” of feedback is always superior to a level-blind sampling approach. We evaluate our method experimentally on three datasets in different domains and observe that with our multi-channel sampler the accuracy of recommendations can be improved considerably compared to the state-of-the-art models. Further experiments reveal that the appropriate sampling method depends on particular properties of datasets such as popularity skewness.
Association Analysis of Deep Genomic Features Extracted by Denoising Autoencoders in Breast Cancer

Artificial intelligence-based unsupervised deep learning (DL) is widely used to mine multimodal big data. However, there are few applications of this technology to cancer genomics. We aim to develop DL models to extract deep features from the breast cancer gene expression data and copy number alteration (CNA) data separately and jointly. We hypothesize that the deep features are associated with patients’ clinical characteristics and outcomes. Two unsupervised denoising autoencoders (DAs) were developed to extract deep features from TCGA (The Cancer Genome Atlas) breast cancer gene expression and CNA data separately and jointly. A heat map was used to view and cluster patients into subgroups based on these DL features. Fisher’s exact test and Pearson’ Chi-square test were applied to test the associations of patients’ groups and clinical information. Survival differences between the groups were evaluated by Kaplan–Meier (KM) curves. Associations between each of the features and patient’s overall survival were assessed using Cox’s proportional hazards (COX-PH) model and a risk score for each feature set from the different omics data sets was generated from the survival regression coefficients. The risk scores for each feature set were binarized into high- and low-risk patient groups to evaluate survival differences using KM curves. Furthermore, the risk scores were traced back to their gene level DAs weights so that the three gene lists for each of the genomic data points were generated to perform gene set enrichment analysis. Patients were clustered into two groups based on concatenated features from the gene expression and CNA data and these two groups showed different overall survival rates (p-value = 0.049) and different ER (Estrogen receptor) statuses (p-value = 0.002, OR (odds ratio) = 0.626). All the risk scores from the gene expression and CNA data and their concatenated one were significantly associated with breast cancer survival. The patients with the high-risk group were significantly associated with patients’ worse outcomes (p-values ≤ 0.0023). The concatenated risk score was enriched by the AMP-activated protein kinase (AMPK) signaling pathway, the regulation of DNA-templated transcription, the regulation of nucleic acid-templated transcription, the regulation of apoptotic process, the positive regulation of gene expression, the positive regulation of cell proliferation, heart morphogenesis, the regulation of cellular macromolecule biosynthetic process, with FDR (false discovery rate) less than 0.05. We confirmed DAs can effectively extract meaningful genomic features from genomic data and concatenating multiple data sources can improve the significance of the features associated with breast cancer patients’ clinical characteristics and outcomes.
Convolutional Neural Network with SVM for Classification of Animal Images

Advances in GPU, parallel computing, and deep neural network made rapid growth in the field of machine learning and computer vision. In this paper, we try to explore the convolution neural network to classify animals. The convolution neural network is a powerful machine learning tool which is trained using a large collection of diverse images. In this paper, we combine convolutional neural network and SVM for classification of animals. The animal images are trained using AlexNet pretrained convolution neural network. Further, the extracted features are fed into multiclass SVM classifier for the purpose of classification. To evaluate the performance of our system, we have conducted extensive experimentation on our own dataset of 5000 images with 50 classes, each class containing 100 images. From the results, we can easily observe that the proposed method has achieved a good classification rate compared to the works in the literature.
Pathology-preserving intensity standardization framework for multi-institutional FLAIR MRI datasets

Fluid-Attenuated Inversion Recovery (FLAIR) MRI are used by physicians to analyze white matter lesions (WML) of the brain, which are related to neurodegenerative diseases such as dementia and vascular disease. To study the causes and progression of these diseases, multi-centre (MC) studies are conducted, with images acquired and analyzed from multiple institutions. Due to differences in acquisition software and hardware, there is variability in image properties, which creates challenges for automated algorithms. This work explores this variability, known as the MC effect, by analyzing nearly 5000 MC FLAIR volumes and proposes an intensity standardization framework to normalize intensity non-standardness in FLAIR MRI, while ensuring the appearance of WML. Results show that original image characteristics varied significantly between scanner vendors and centres, and that this variability was reduced with standardization. To further highlight the utility of intensity standardization, a threshold-based brain extraction algorithm is implemented and compared with a classifier-based approach. A competitive Dice Similarity Coefficient of 81% was achieved on 183 volumes, demonstrating that optimized pre-processing can effectively reduce the variability in MC studies, allowing for simplified algorithms to be applied on large datasets robustly.
Persistence of data-driven knowledge to predict breast cancer survival

Machine learning predictive models for breast cancer survival can improve if they are made specific to the stage of the cancer at the time of diagnosis. However, the relevance of the clinical parameters in that prediction, and the predictive quality of these models may change over time. To determine whether the findings on the influence of clinical parameters and the performance of machine learning models in the prediction of breast cancer survival have to be considered temporary or permanent, and if temporary what is the period of validity of the new generated knowledge. Fifteen recently published relevant conclusions on the application of machine learning methods to predict breast cancer survival were identified. Then, the data on breast cancer in the SEER database were used to construct several data-driven models over time to predict five-year survival of breast cancer. Three different machine learning methods were used. Stage-specific models and joint models for all the stages were considered. The predictive quality of the models and the importance of clinical parameters were subjected to a persistence analysis over time in order to determine the validity and durability of these fifteen conclusions. Only 53% of the conclusions were true for the SEER cases in 1988–2009, and only 75% of these were true over time. Relevant conclusions such as the impossibility to improve survival prediction of the most frequent stages with more data or the importance of the grade of the cancer to predict breast cancer survival of patients with distant metastasis turned to be false when subjected to a temporal analysis. Our study concludes that data-driven knowledge obtained with machine learning methods must be subject to over time validation before it can be clinically and professionally applied.
Offline Signature Identification and Verification Using Capsule Network

In offline signature identification and verification systems, hand -crafted feature extraction methods, such as local binary patterns, have recently been set aside for automatic feature extraction methods such as convolutional neural networks (CNN). Although these CNN-based algorithms often obtain satisfying results, they require either many samples to find the best data representations or pre-trained network weights. To obviate the necessity of many samples as well as pre-trained weights, Capsule Network has recently claimed to achieve the best data representation using only a limited amount of data. This network not only obtains many variations of limited input samples via affine transformations in the algorithm but also uses hierarchical layers to select the most informative features without losing the exact informational position of the others. It is from this point of view that this paper first aims to evaluate performances of Capsule Network and the CNN-based equivalent model for the signature identification task. This evaluation is done under two lower resolutions than is usual to understand whether texture patterns are still staying as informative as they usually are for both algorithms. While Capsule Network achieves 98,8% and 98,6 % accuracies for 64×64 and 32×32 input resolutions, respectively, CNN obtains 55,4% and 54,7% accuracies. The second aim of the paper is to generalize the capability of Capsule Network concerning the verification task. Through this evaluation, the capability of Capsule Network is shown to obtain better feature extraction and classification results compared to the CNN-based equivalent model for the verification task as well as the identification task.
Beyond Product Quantization: Deep Progressive Quantization for Image Retrieval

Product Quantization (PQ) has long been a mainstream for generating an exponentially large codebook at very low memory/time cost. Despite its success, PQ is still tricky for the decomposition of high-dimensional vector space, and the retraining of model is usually unavoidable when the code length changes. In this work, we propose a deep progressive quantization (DPQ) model, as an alternative to PQ, for large scale image retrieval. DPQ learns the quantization codes sequentially and approximates the original feature space progressively. Therefore, we can train the quantization codes with different code lengths simultaneously. Specifically, we first utilize the label information for guiding the learning of visual features, and then apply several quantization blocks to progressively approach the visual features. Each quantization block is designed to be a layer of a convolutional neural network, and the whole framework can be trained in an end-to-end manner. Experimental results on the benchmark datasets show that our model significantly outperforms the state-of-the-art for image retrieval. Our model is trained once for different code lengths and therefore requires less computation time. Additional ablation study demonstrates the effect of each component of our proposed model. Our code is released at https://github.com/cfm-uestc/DPQ.
A Recursive Subdivision Technique for Sampling Multi-class Scatterplots

We present a non-uniform recursive sampling technique for multi-class scatterplots, with the specific goal of faithfully presenting relative data and class densities, while preserving major outliers in the plots. Our technique is based on a customized binary kd-tree, in which leaf nodes are created by recursively subdividing the underlying multi-class density map. By backtracking, we merge leaf nodes until they encompass points of all classes for our subsequently applied outlier-aware multi-class sampling strategy. A quantitative evaluation shows that our approach can better preserve outliers and at the same time relative densities in multi-class scatterplots compared to the previous approaches, several case studies demonstrate the effectiveness of our approach in exploring complex and real world data.
DENS: A Dataset for Multi-class Emotion Analysis

We introduce a new dataset for multi-class emotion analysis from long-form narratives in English.The Dataset for Emotions of Narrative Sequences (DENS) was collected from both classic literature available on Project Gutenberg and modern online narratives available on Wattpad, annotated using Amazon Mechanical Turk.A number of statistics and baseline benchmarks are provided for the dataset.Of the tested techniques, we find that the fine-tuning of a pre-trained BERT model achieves the best results, with an average micro-F1 score of 60.4%.Our results show that the dataset provides a novel opportunity in emotion analysis that requires moving beyond existing sentence-level techniques.
Earlier Isn’t Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization

Despite the recent developments on neural summarization systems, the underlying logic behind the improvements from the systems and its corpus-dependency remains largely unexplored.Position of sentences in the original text, for example, is a well known bias for news summarization.Following in the spirit of the claim that summarization is a combination of sub-functions, we define three sub-aspects of summarization: position, importance, and diversity and conduct an extensive analysis of the biases of each sub-aspect with respect to the domain of nine different summarization corpora (e.g., news, academic papers, meeting minutes, movie script, books, posts).We find that while position exhibits substantial bias in news articles, this is not the case, for example, with academic papers and meeting minutes.Furthermore, our empirical study shows that different types of summarization systems (e.g., neural-based) are composed of different degrees of the sub-aspects.Our study provides useful lessons regarding consideration of underlying sub-aspects when collecting a new summarization dataset or developing a new system.
Transductive Zero-Shot Learning with Visual Structure Constraint

To recognize objects of the unseen classes, most existing Zero-Shot Learning (ZSL) methods first learn a compatible projection function between the common semantic space and the visual space based on the data of source seen classes, then directly apply it to the target unseen classes. However, in real scenarios, the data distribution between the source and target domain might not match well, thus causing the well-known domain shift problem. Based on the observation that visual features of test instances can be separated into different clusters, we propose a new visual structure constraint on class centers for transductive ZSL, to improve the generality of the projection function (\ie alleviate the above domain shift problem). Specifically, three different strategies (symmetric Chamfer-distance,Bipartite matching distance, and Wasserstein distance) are adopted to align the projected unseen semantic centers and visual cluster centers of test instances. We also propose a new training strategy to handle the real cases where many unrelated images exist in the test dataset, which is not considered in previous methods. Experiments on many widely used datasets demonstrate that the proposed visual structure constraint can bring substantial performance gain consistently and achieve state-of-the-art results.
Local Path Planning Algorithm for Blind-guiding Robot Based on Improved DWA Algorithm

When Dynamic Window Approach (DWA) is used in obstacle avoidance of blind-guiding robots, the contradiction which between the heading and velocity evaluation factors is not considered, resulting in, selecting the trajectory under certain road conditions, the not-timely collision avoidance, frequently direction changing, time-consuming planning and other issues in the planned path. To balance the relationship between the original three evaluation factors, the evaluation factor about the change of orientation is introduced into the function of path evaluation, which will suppress the excessive influence of a particular factor on the evaluation function under some specific circumstances, and to reduce unnecessary steering frequency of the robot. Experiments reveal that the actual required runtime of the planning path with the improved algorithm is reduced at average of 45.37% compared with that with the DWA algorithm, path planned with the improved algorithm can be planned advance to avoid obstacles with a smaller and continuous curvature. It can be obtained that the improved algorithm has a smoother trajectory and more timely collision avoidance, which can meet the comfort requirements of the blind-guiding robot's users.
Parallel Processing of Improved KNN Text Classification Algorithm Based on Hadoop

With the rapid development of mobile Internet, the network has become an important medium for people to exchange information. The research on text classification has practical significance. Using the Hadoop platform to parallelize the KNN classification algorithm can quickly and accurately classify the text, but when calculating the similarity or distance of the sample points, the KNN algorithm will increase with the increase of the sample data, which will lead to the algorithm time. Increased complexity and reduced classification accuracy. Therefore, Parallel Processing of Improved KNN text classification algorithm based on Hadoop platform is proposed. The CLARA clustering algorithm is used to cut out the samples with low similarity in the dataset, and the calculation of sample distance in the dataset is reduced. Then design the parallel KNN MapReduce program to classify the network public opinion data. The experimental results show that the improved parallel KNN algorithm improves the accuracy and time of text classification.
Quantifying Memory Underutilization in HPC Systems and Using it to Improve Performance via Architecture Support

A system's memory size is often dictated by worst-case workloads with highest memory requirements; this causes memory to be underutilized in the common case when the system is not running its worst-case workloads. Cognizant of this memory underutilization problem, many prior works have studied memory utilization and explored how to improve it in the context of cloud.
Learning to Navigate

Navigation is an important cognitive task that enables humans and animals to traverse, with or without maps, over long distances in the complex world. Such long-range navigation can simultaneously support self-localisation ("I am here") and a representation of the goal ("I am going there"). For this reason, studying navigation is fundamental to the study and development of artificial intelligence, and trying to replicate navigation in artificial agents can also help neuroscientists understand its biological underpinnings. This talk will cover our own journey to understand navigation by building deep reinforcement learning agents, starting from learning to control a simple agent that can explore and memorise large 3D mazes to designing agents with a read-write memory that can generalise to unseen mazes from one traversal. I will show how these artificial agents relate to navigation in the real world, both through the study of the emergence of grid cell representations in neural networks and by demonstrating that these agents can navigate in Street View-based real world photographic environments. I will finally present two approaches in our ongoing work on leveraging multimodal information for generalising navigation policies to unseen environments in Street View, one consisting in following language instructions and the second one in transferring navigation policies by training on aerial views.
Optimizing Fraudulent Firm Prediction Using Ensemble Machine Learning: A Case Study of an External Audit

This paper is a case study of utilizing machine learning for developing a decision-making system for auditors before initializing the audit fieldwork of public firms. Annual data of 777 firms from 14 different sectors are collected and a MCTOPE (Multi criteria ToPsis based Ensemble) framework is implemented to build an ensemble classifier. MCTOPE framework optimizes the performance of classification during ensemble building using the TOPSIS multi-criteria decision-making algorithm. Ensemble machine learning is used for optimizing the prediction performance of suspicious firm predictor in the previous work available at https://www.tandfonline.com/doi/full/10.1080/08839514.2018.1451032. After achieving an accuracy of 94.6% and AUC (area under the curve) value of 0.98, this ensemble classifier is employed in a web application developed for auditors using Python and R script for the prediction of suspicious firm before planning an external audit. The performance of an ensemble classifier is validated using K-fold cross validation technique and is found to be better than the state-of-the-art classifiers.
Natural language processing for disease phenotyping in UK primary care records for research: a pilot study in myocardial infarction and death

Abstract Background Free text in electronic health records (EHR) may contain additional phenotypic information beyond structured (coded) information. For major health events – heart attack and death – there is a lack of studies evaluating the extent to which free text in the primary care record might add information. Our objectives were to describe the contribution of free text in primary care to the recording of information about myocardial infarction (MI), including subtype, left ventricular function, laboratory results and symptoms; and recording of cause of death. We used the CALIBER EHR research platform which contains primary care data from the Clinical Practice Research Datalink (CPRD) linked to hospital admission data, the MINAP registry of acute coronary syndromes and the death registry. In CALIBER we randomly selected 2000 patients with MI and 1800 deaths. We implemented a rule-based natural language engine, the Freetext Matching Algorithm, on site at CPRD to analyse free text in the primary care record without raw data being released to researchers. We analysed text recorded within 90 days before or 90 days after the MI, and on or after the date of death. Results We extracted 10,927 diagnoses, 3658 test results, 3313 statements of negation, and 850 suspected diagnoses from the myocardial infarction patients. Inclusion of free text increased the recorded proportion of patients with chest pain in the week prior to MI from 19 to 27%, and differentiated between MI subtypes in a quarter more patients than structured data alone. Cause of death was incompletely recorded in primary care; in 36% the cause was in coded data and in 21% it was in free text. Only 47% of patients had exactly the same cause of death in primary care and the death registry, but this did not differ between coded and free text causes of death. Conclusions Among patients who suffer MI or die, unstructured free text in primary care records contains much information that is potentially useful for research such as symptoms, investigation results and specific diagnoses. Access to large scale unstructured data in electronic health records (millions of patients) might yield important insights.