Reviewerly/exHarmony huggingface dataset:
Obtaining Knowledge in Pathology Reports Through a Natural Language Processing Approach With Classification, Named-Entity Recognition, and Relation-Extraction Heuristics

Robust institutional tumor banks depend on continuous sample curation or else subsequent biopsy or resection specimens are overlooked after initial enrollment. Curation automation is hindered by semistructured free-text clinical pathology notes, which complicate data abstraction. Our motivation is to develop a natural language processing method that dynamically identifies existing pathology specimen elements necessary for locating specimens for future use in a manner that can be re-implemented by other institutions.Pathology reports from patients with gastroesophageal cancer enrolled in The University of Chicago GI oncology tumor bank were used to train and validate a novel composite natural language processing-based pipeline with a supervised machine learning classification step to separate notes into internal (primary review) and external (consultation) reports; a named-entity recognition step to obtain label (accession number), location, date, and sublabels (block identifiers); and a results proofreading step.We analyzed 188 pathology reports, including 82 internal reports and 106 external consult reports, and successfully extracted named entities grouped as sample information (label, date, location). Our approach identified up to 24 additional unique samples in external consult notes that could have been overlooked. Our classification model obtained 100% accuracy on the basis of 10-fold cross-validation. Precision, recall, and F1 for class-specific named-entity recognition models show strong performance.Through a combination of natural language processing and machine learning, we devised a re-implementable and automated approach that can accurately extract specimen attributes from semistructured pathology notes to dynamically populate a tumor registry.
An Overview on Image Sentiment Analysis: Methods, Datasets and Current Challenges

This paper introduces the research field of Image Sentiment Analysis, analyses the related problems, provides an in-depth overview of current research progress, discusses the major issues and outlines the new opportunities and challenges in this area. An overview of the most significant works is presented. A discussion about the related specific issues is provided: emotions representation models, existing datasets and most used features. A generalizable analysis of the problem is also presented, by identifying and analyzing the components that affect the sentiment toward an image. Furthermore, the paper introduces some additional challenges and techniques that could be investigated, proposing suggestions for new methods, features and datasets.
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement

This paper studies Learning from Observations (LfO) for imitation learning with access to state-only demonstrations. In contrast to Learning from Demonstration (LfD) that involves both action and state supervisions, LfO is more practical in leveraging previously inapplicable resources (e.g., videos), yet more challenging due to the incomplete expert guidance. In this paper, we investigate LfO and its difference with LfD in both theoretical and practical perspectives. We first prove that the gap between LfD and LfO actually lies in the disagreement of inverse dynamics models between the imitator and expert, if following the modeling approach of GAIL. More importantly, the upper bound of this gap is revealed by a negative causal entropy which can be minimized in a model-free way. We term our method as Inverse-Dynamics-Disagreement-Minimization (IDDM) which enhances the conventional LfO method through further bridging the gap to LfD. Considerable empirical results on challenging benchmarks indicate that our method attains consistent improvements over other LfO counterparts.
Rethinking Attribute Representation and Injection for Sentiment Classification

Text attributes, such as user and product information in product reviews, have been used to improve the performance of sentiment classification models.The de facto standard method is to incorporate them as additional biases in the attention mechanism, and more performance gains are achieved by extending the model architecture.In this paper, we show that the above method is the least effective way to represent and inject attributes.To demonstrate this hypothesis, unlike previous models with complicated architectures, we limit our base model to a simple BiLSTM with attention classifier, and instead focus on how and where the attributes should be incorporated in the model.We propose to represent attributes as chunk-wise importance weight matrices and consider four locations in the model (i.e., embedding, encoding, attention, classifier) to inject attributes.Experiments show that our proposed method achieves significant improvements over the standard approach and that attention mechanism is the worst location to inject attributes, contradicting prior work.We also outperform the state-of-the-art despite our use of a simple base model.Finally, we show that these representations transfer well to other tasks 1 .
Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation

Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information.However, little attention has been paid to this problem.To bridge this gap we propose a model named Fidelity-oriented Product Description Generator (FPDG).FPDG takes the entity label of each word into account, since the product attribute information is always conveyed by entity words.Specifically, we first propose a Recurrent Neural Network (RNN) decoder based on the Entity-labelguided Long Short-Term Memory (ELSTM) cell, taking both the embedding and the entity label of each word as input.Second, we establish a keyword memory that stores the entity labels as keys and keywords as values, and FPDG will attend to keywords through attending to their entity labels.Experiments conducted a large-scale real-world product description dataset show that our model achieves the state-of-the-art performance in terms of both traditional generation metrics as well as human evaluations.Specifically, FPDG increases the fidelity of the generated descriptions by 25%.
L_DMI: An Information-theoretic Noise-robust Loss Function

Accurately annotating large scale dataset is notoriously expensive both in time and in money. Although acquiring low-quality-annotated dataset can be much cheaper, it often badly damages the performance of trained models when using such dataset without particular treatment. Various methods have been proposed for learning with noisy labels. However, most methods only handle limited kinds of noise patterns, require auxiliary information or steps (e.g. , knowing or estimating the noise transition matrix), or lack theoretical justification. In this paper, we propose a novel information-theoretic loss function, $\mathcal{L}_{DMI}$, for training deep neural networks robust to label noise. The core of $\mathcal{L}_{DMI}$ is a generalized version of mutual information, termed Determinant based Mutual Information (DMI), which is not only information-monotone but also relatively invariant. \emph{To the best of our knowledge, $\mathcal{L}_{DMI}$ is the first loss function that is provably robust to instance-independent label noise, regardless of noise pattern, and it can be applied to any existing classification neural networks straightforwardly without any auxiliary information}. In addition to theoretical justification, we also empirically show that using $\mathcal{L}_{DMI}$ outperforms all other counterparts in the classification task on both image dataset and natural language dataset include Fashion-MNIST, CIFAR-10, Dogs vs. Cats, MR with a variety of synthesized noise patterns and noise amounts, as well as a real-world dataset Clothing1M. Codes are available at https://github.com/Newbeeer/L_DMI .
Mobile robot navigation with the combination of supervised learning in cerebellum and reward-based learning in basal ganglia

Autonomous navigation of mobile robot in unknown environment has attracted much attention of scholars over the past decades, and many bio-inspired heuristic navigation models have been presented, such as the cerebellum and basal ganglia models. Pervious cerebellum and basal ganglia model, however, treats them as parallel and independent system, without interaction between them, or combines their function together, but with only unidirectional communication between them. Based on the cognition and developmental mechanism of the biology, this paper proposes a novel navigation model, which uses the motivated developmental network (MDN) to mimic the supervised learning of the cerebellum and the reinforcement learning based on the radial basis function neural network (RBFNN) to simulate the reward-based learning of the basal ganglia, and integrates them together to construct a hybrid complex cognition model, to navigate a mobile robot in unknown environment. During the environment exploration, for the unexplored places, the artificial agent uses the cerebellum model to choose action, instead of the ∊-greedy method, to accelerate the learning convergence speed of the basal ganglia. For the explored places, it directly uses the basal ganglia based on the RBFNN to explore, then update and perfect the knowledge base of the cerebellum, which enable the cerebellum to achieve better decision in the following exploration. Hence, it realizes not only the two way communication between the cerebellum and basal ganglia, but also the co-development of them. Experimental results show that this model can enable the agent to autonomously development its intelligence through the hybrid learning. As far as we know, this is the first work to realize the direct communication between the basal ganglia and the cerebellum.
Challenges of Privacy-Preserving Machine Learning in IoT

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. However, the extensive data collection and processing in IoT also engender various privacy concerns. This paper provides a taxonomy of the existing privacy-preserving machine learning approaches developed in the context of cloud computing and discusses the challenges of applying them in the context of IoT. Moreover, we present a privacy-preserving inference approach that runs a lightweight neural network at IoT objects to obfuscate the data before transmission and a deep neural network in the cloud to classify the obfuscated data. Evaluation based on the MNIST dataset shows satisfactory performance.
An effective classifier based on convolutional neural network and regularized extreme learning machine

An effective classifier combining convolutional neural network and regularized extreme learning machine (called as CNN-RELM) is presented in this paper. Firstly, CNN-RELM trains the convolutional neural network (CNN) using the gradient descent method until the learning target accuracy reaches. Then the fully connected layer of CNN is replaced by regularized extreme learning machine (RELM) optimized by genetic algorithm and the rest layers of the CNN remain unchanged. The experiments on different face databases are given to evaluate the performance of CNN-RELM. The experimental results show that CNN-RELM is a feasible classifier and it outperforms CNN and RELM. Due to the uniting of CNN and RELM, CNN-RELM have the advantages of CNN and RELM and it is easier to learn and faster in testing.
Facilitating the elicitation of beliefs for use in Bayesian Belief modelling

Expert opinion is increasingly being used to inform Bayesian Belief Networks, in particular to define the conditional dependencies modelled by the graphical structure. The elicitation of such expert opinion remains a major challenge due to both the quantity of information required and the ability of experts to quantify subjective beliefs effectively. In this work, we introduce a method designed to initialise conditional probability tables based on a small number of simple questions that capture the overall shape of a conditional probability distribution before enabling the expert to refine their results in an efficient way. These methods have been incorporated into a software Application for Conditional probability Elicitation (ACE), freely available at https://github.com/KirstyLHassall/ACE (Hassall, 2019).
Augmenting Neural Machine Translation through Round-Trip Training Approach

Abstract The quality of Neural Machine Translation (NMT), as a data-driven approach, massively depends on quantity, quality and relevance of the training dataset. Such approaches have achieved promising results for bilingually high-resource scenarios but are inadequate for low-resource conditions. Generally, the NMT systems learn from millions of words from bilingual training dataset. However, human labeling process is very costly and time consuming. In this paper, we describe a round-trip training approach to bilingual low-resource NMT that takes advantage of monolingual datasets to address training data bottleneck, thus augmenting translation quality. We conduct detailed experiments on English-Spanish as a high-resource language pair as well as Persian-Spanish as a low-resource language pair. Experimental results show that this competitive approach outperforms the baseline systems and improves translation quality.
Sentiment Analysis of Bangla Song Review- A Lexicon Based Backtracking Approach

In the last decade, Sentiment analysis is a prospering experimental topic of research as because of a lot of opinionated data accessible on Blogs & social networking sites. It is the reference to the assignment of Natural Language Processing to decide whether text or content contains any subjective information like positive, negative or not. These social media and other online platforms are giving an immense stage to uncover human's gifts in a fast speed, and average citizens can likewise put their feeling through the remarks which emphatically show how they are accepting another potential. In this paper, we have presented a sentimental analysis of Bengali song reviews from a specific YouTube channel to analyse people acceptance rate of a new young star. For detecting the sentiments, we have used a backtracking algorithm, where the heart of this approach is a sentiment lexicon. And the research showed the backtracking algorithm performed more than 70% accuracy to detect actual public sentiment.
Simpler quantum counting

A simpler quantum counting algorithm based on amplitude amplification is presented. This algorithm is bounded by O(sqrt(N/M)) calls to the controlled-Grover operator where M is the number of marked states and N is the total number of states in the search space. This algorithm terminates within log(sqrt(N/M)) consecutive measurement steps when the probability p1 of measuring the state |1&gt; is at least 0.5, and the result from the final step is used in estimating M by a classical post processing. The purpose of controlled-Grover iteration is to increase the probability p1. This algorithm requires less quantum resources in terms of the width and depth of the quantum circuit, produces a more accurate estimate of M, and runs significantly faster than the phase estimation-based quantum counting algorithm when the ratio M/N is small. We compare the two quantum counting algorithms by simulating various cases with a different M/N ratio, such as M/N &gt; 0.125 or M/N &lt; 0.001.
Table Tennis Stroke Recognition Based on Body Sensor Network

Table tennis stroke recognition is very important for athletes to analyze their sports skills. It can help players to regulate hitting movement and calculate sports consumption. Different players have different stroke motions, which makes stroke recognition more difficult. In order to accurately distinguish the stroke movement, this paper uses body sensor networks (BSN) to collect motion data. Sensors collecting acceleration and angular velocity information are placed on the upper arm, lower arm and back respectively. Principal component analysis (PCA) is employed to reduce the feature dimensions and support vector machine (SVM) is used to recognize strokes. Compared with other classification algorithms, the final experimental results (97.41% accuracy) illustrate that the algorithm proposed in the paper is effective and useful.
Making at the Margins

HCI and CSCW literature has extensively studied a wide variety of maker cultures. In this paper, we focus on understanding what making is like for people and communities who do not have access to advanced technological infrastructures. We report on six-month-long ethnographic fieldwork at a non-profit, resource-constrained, e-waste recycling centre that engages members from a low socioeconomic status (SES) community in making activities. Our findings show that making in such a setting is shaped by local economic and social factors in a resource-constrained environment and highlight how this community engages in a wide range of making activities. In describing these making activities, we emphasize how making was conducted to purposely enable ongoing and future making by others; promoted the wellbeing and skill development of centre members; and was socially-engaged to address concerns in the local community. We conclude by discussing how such type of making contributes a new understanding of maker culture, one that is appreciative of resource-constraints, integrates different sources of value, and is embedded in local place.
Visual Link Routing in Immersive Visualisations

In immersive display environments, such as virtual or augmented reality, we can make explicit the connections between data points in visualisations and their context in the world, or in other visualisations. This paper considers the requirements and design space for drawing such links in order to minimise occlusion and clutter. A novel possibility in immersive environments is to optimise the link layout with respect to a particular point of view. In collaborative scenarios there is the need to do this for multiple points of view. We present an algorithm to achieve such link layouts and demonstrate its applicability in a variety of practical use cases.
On Neural Architecture Search for Resource-Constrained Hardware Platforms

In the recent past, the success of Neural Architecture Search (NAS) has enabled researchers to broadly explore the design space using learning-based methods. Apart from finding better neural network architectures, the idea of automation has also inspired to improve their implementations on hardware. While some practices of hardware machine-learning automation have achieved remarkable performance, the traditional design concept is still followed: a network architecture is first structured with excellent test accuracy, and then compressed and optimized to fit into a target platform. Such a design flow will easily lead to inferior local-optimal solutions. To address this problem, we propose a new framework to jointly explore the space of neural architecture, hardware implementation, and quantization. Our objective is to find a quantized architecture with the highest accuracy that is implementable on given hardware specifications. We employ FPGAs to implement and test our designs with limited loop-up tables (LUTs) and required throughput. Compared to the separate design/searching methods, our framework has demonstrated much better performance under strict specifications and generated designs of higher accuracy by 18\% to 68\% in the task of classifying CIFAR10 images. With 30,000 LUTs, a light-weight design is found to achieve 82.98\% accuracy and 1293 images/second throughput, compared to which, under the same constraints, the traditional method even fails to find a valid solution.
Context-Aware Conversation Thread Detection in Multi-Party Chat

In multi-party chat, it is common for multiple conversations to occur concurrently, leading to intermingled conversation threads in chat logs.In this work, we propose a novel Context-Aware Thread Detection (CATD) model that automatically disentangles these conversation threads.We evaluate our model on three realworld datasets and demonstrate an overall improvement in thread detection accuracy over state-of-the-art benchmarks.
Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-Translation

Neural Machine Translation (NMT) models have been proved strong when translating clean texts, but they are very sensitive to noise in the input. Improving NMT models robustness can be seen as a form of "domain" adaption to noise. The recently created Machine Translation on Noisy Text task corpus provides noisy-clean parallel data for a few language pairs, but this data is very limited in size and diversity. The state-of-the-art approaches are heavily dependent on large volumes of back-translated data. This paper has two main contributions: Firstly, we propose new data augmentation methods to extend limited noisy data and further improve NMT robustness to noise while keeping the models small. Secondly, we explore the effect of utilizing noise from external data in the form of speech transcripts and show that it could help robustness.
Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context

Conversations have an intrinsic one-to-many property, which means that multiple responses can be appropriate for the same dialog context. In task-oriented dialogs, this property leads to different valid dialog policies towards task completion. However, none of the existing task-oriented dialog generation approaches takes this property into account. We propose a Multi-Action Data Augmentation (MADA) framework to utilize the one-to-many property to generate diverse appropriate dialog responses. Specifically, we first use dialog states to summarize the dialog history, and then discover all possible mappings from every dialog state to its different valid system actions. During dialog system training, we enable the current dialog state to map to all valid system actions discovered in the previous process to create additional state-action pairs. By incorporating these additional pairs, the dialog policy learns a balanced action distribution, which further guides the dialog model to generate diverse responses. Experimental results show that the proposed framework consistently improves dialog policy diversity, and results in improved response diversity and appropriateness. Our model obtains state-of-the-art results on MultiWOZ.