Reviewerly/exHarmony huggingface dataset:
Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology

Abstract A model’s ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a clinically-oriented approach to uncertainty quantification for whole-slide images, estimating uncertainty using dropout and calculating thresholds on training data to establish cutoffs for low- and high-confidence predictions. We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without uncertainty, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that uncertainty thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts.
Dry bean cultivars classification using deep cnn features and salp swarm algorithm based extreme learning machine

Since dry bean varieties have different qualities and economic values, their separation is of great importance in the field of agriculture. In recent years, the use of artificial intelligence-supported and image-based systems has become widespread for this process. This study aims to create a data set consisting of 14 classes in the detection of dry beans and to investigate the effectiveness of the hybrid structure of the extreme learning machine (ELM) model with GoogLeNet transfer learning on this dataset. At the same time, the salp swarm algorithm (SSA), which is one of the swarm intelligence algorithms, was used to test its applicability in ELM classifier by optimizing ELM parameters. The performance of these models was compared with ELM-based particle swarm optimization, harris hawks optimization, artificial bee colony, and traditional machine learning algorithms such as support vector machine and k-nearest neighbor. The suggested SSA-ELM model successfully classifies 14 different types of dry beans with a success rate of 91.43%. The comparable results demonstrate that the proposed hybrid model had better classification accuracy and performance metrics than traditional machine learning algorithms. In addition, it is seen that the use of image data, extraction of deep features, and classification with optimized ELM in the classification of dry beans have achieved comparable success in the literature.
A Supervised Approach to Predict Company Acquisition with Factual and Topic Features Using Profiles and News Articles on TechCrunch

Merger and Acquisition (M&amp;A) prediction has been an interesting and challenging research topic in the past a few decades. However, past work has only adopted numerical features in building models, and yet the valuable textual information from the great variety of social media sites has not been touched at all. To fully explore this information, we used the profiles and news articles for companies and people on TechCrunch, the leading and largest public database for the tech world, which anybody can edit. Specifically, we explored topic features via topic modeling techniques, as well as a set of other novel features of our design within a machine learning framework. We conducted experiments of the largest scale in the literature, and achieved a high true positive rate (TP) between 60% to 79.8% with a false positive rate (FP) mostly between 0% and 8.3% over company categories with a small number of missing attributes in the CrunchBase profiles.
Image Deblurring with a Class-Specific Prior

A fundamental problem in image deblurring is to recover reliably distinct spatial frequencies that have been suppressed by the blur kernel. To tackle this issue, existing image deblurring techniques often rely on generic image priors such as the sparsity of salient features including image gradients and edges. However, these priors only help recover part of the frequency spectrum, such as the frequencies near the high-end. To this end, we pose the following specific questions: (i) Does any image class information offer an advantage over existing generic priors for image quality restoration? (ii) If a class-specific prior exists, how should it be encoded into a deblurring framework to recover attenuated image frequencies? Throughout this work, we devise a class-specific prior based on the band-pass filter responses and incorporate it into a deblurring strategy. More specifically, we show that the subspace of band-pass filtered images and their intensity distributions serve as useful priors for recovering image frequencies that are difficult to recover by generic image priors. We demonstrate that our image deblurring framework, when equipped with the above priors, significantly outperforms many state-of-the-art methods using generic image priors or class-specific exemplars.
Vehicle Classification Based on Seismic Signatures Using Convolutional Neural Network

Seismic signals can be used for vehicle classification. However, this task becomes difficult as a result of various noises. Convolutional neural networks (CNNs) have been employed successfully in many fields as a result of its ability to learn low-/mid-/high-level features. This letter investigates the application of CNN to classify vehicles by means of the seismic trace that the geophone recorded. The study has two primary contributions. First, a deep CNN architecture for vehicle classification by seismic signal is proposed. Second, considering the similarities between speech recognition and vehicle classification based on seismic signal, log-scaled frequency cepstral coefficient (LFCC) matrix is proposed to extract features of seismic signals as the input of CNN. The data from DARPA's SensIt project, which contain seismic signals from two kinds of vehicles, are used to evaluate the method. By combining the proposed LFCC matrix and CNN architecture, the algorithm produces a state-of-the-art result compared with other methods.
Improved Alpha-Guided Grey Wolf Optimizer

Grey wolf optimizer (GWO) is a new meta-heuristic swarm intelligence algorithm, which has shown promising performance in solving optimization problems. In order to improve the convergence speed of GWO, an alpha-guided GWO (AgGWO), in which the evolving process of the population is guided by the update direction of alpha (best solution), is proposed. However, in the AgGWO, its evolutionary guidance mechanism makes the algorithm more likely to fall into the local optimal solution and the fixed value of theta may not be suitable for all problems optimization. To overcome these shortcomings and simplify its structure, an improved AgGWO (IAgGWO) is proposed in this paper. In the IAgGWO, the update direction of alpha is used to guide the evolving process of alpha, beta (second best solution), and delta (third best solution), and A and C are the coefficient scalars instead of coefficient vectors in the original algorithm. Therefore, a mutation operator is introduced to further enhance the exploration. The advantageous performance of the IAgGWO is validated by comparisons with other four algorithms on 35 benchmark functions and the engineering problem of two-stage operational amplifier design.
Joint Event Extraction Based on Hierarchical Event Schemas From FrameNet

Event extraction is useful for many practical applications, such as news summarization and information retrieval.However, the popular automatic context extraction (ACE) event extraction program only defines very limited and coarse event schemas, which may not be suitable for practical applications.FrameNet is a linguistic corpus that defines complete semantic frames and frame-to-frame relations.As frames in FrameNet share highly similar structures with event schemas in ACE and many frames actually express events, we propose to redefine the event schemas based on FrameNet.Specifically, we extract frames expressing event information from FrameNet and leverage the frame-to-frame relations to build a hierarchy of event schemas that are more fine-grained and have much wider coverage than ACE.Based on the new event schemas, we propose a joint event extraction approach that leverages the hierarchical structure of event schemas and frame-to-frame relations in FrameNet.The extensive experiments have verified the advantages of our hierarchical event schemas and the effectiveness of our event extraction model.We further apply the results of our event extraction model on news summarization.The results show that the summarization approach based on our event extraction model achieves significant better performance than several state-ofthe-art summarization approaches, which also demonstrates that the hierarchical event schemas and event extraction model are promising to be used in the practical applications.
Research on trust mechanism of cooperation innovation with big data processing based on blockchain

In recent years, the status of cooperation and innovation has become increasingly important. Research on the behavior of production, education, and research organizations has attracted widespread attention. Blockchain technology is considered as another subversive technology following cloud computing, wireless sensor networks, and big data. It is highly concerned by governments, financial institutions, and technology companies. Blockchain technology is essentially a technical solution that collectively maintains a reliable database through a decentralized, high-trust way. Blockchain WEB technology has not only used in financial and other fields, but also has a great potential for application in the fields of production, education, and research. It has expected to play an important role in the establishment of a trust mechanism in the “Internet +” production, research, and innovation and promote education. Drawing on the experience in blockchain applications in the financial sector, the blockchain in the education sector mainly embodies six application modes: building individual science credit data, creating an intelligent education Taobao platform, developing a degree certificate system, and building a new ecology of open educational resources. Of course, due to the uniqueness and complexity of the trust field in the cooperation innovation of industry, universities, and research institutes, the application of blockchain technology is also faced with difficulties in the application, promotion, and operation of production and research, fuzzy ownership of educational big data, limited data storage space, and potential safety problems of blockchain technology. There are many challenges such as the privacy protection risks of teachers and students.
VLSP Shared Task: Named Entity Recognition

Named entities (NE) are phrases that contain the names of persons, organizations, locations, times and quantities, monetary values, percentages, etc. Named Entity Recognition (NER) is the task of recognizing named entities in documents. NER is an important subtask of Information Extraction, which has attracted researchers all over the world since 1990s. For Vietnamese language, although there exists some research projects and publications on NER task before 2016, no systematic comparison of the performance of NER systems has been done. In 2016, the organizing committee of the VLSP workshop decided to launch the first NER shared task, in order to get an objective evaluation of Vietnamese NER systems and to promote the development of high quality systems. As a result, the first dataset with morpho-syntactic and NE annotations has been released for benchmarking NER systems. At VLSP 2018, the NER shared task has been organized for the second time, providing a bigger dataset containing texts from various domains, but without morpho-syntactic annotation. These resources are available for research purpose via the VLSP website vlsp.org.vn/resources. In this paper, we describe the datasets as well as the evaluation results obtained from these two campaigns.
Perceived Intelligence and Perceived Anthropomorphism of Personal Intelligent Agents: Scale Development and Validation

Personal intelligent agents are systems that are autonomous, aware of their environment, continuously learning and adapting to change, able to interact using natural language and capable of completing tasks within a favorable timeframe in a proactive manner.Examples include Siri and Alexa.Several unique characteristics distinguish these agents from other traditional information systems.Of particular interest in this work are characteristics of intelligence and anthropomorphism.This paper describes the process of developing two new measures with satisfactory psychometric properties that can be adapted by researchers to assess the users' perceptions of intelligence and anthropomorphism of PIAs.The measures are validated using data collected from 232 experienced PIA users.
Contextual Dialogue Act Classification for Open-Domain Conversational Agents

Classifying the general intent of the user utterance in a conversation, also known as Dialogue Act (DA), e.g., open-ended question, statement of opinion, or request for an opinion, is a key step in Natural Language Understanding (NLU) for conversational agents. While DA classification has been extensively studied in human-human conversations, it has not been sufficiently explored for the emerging open-domain automated conversational agents. Moreover, despite significant advances in utterance-level DA classification, full understanding of dialogue utterances requires conversational context. Another challenge is the lack of available labeled data for open-domain human-machine conversations. To address these problems, we propose a novel method, CDAC (Contextual Dialogue Act Classifier), a simple yet effective deep learning approach for contextual dialogue act classification. Specifically, we use transfer learning to adapt models trained on human-human conversations to predict dialogue acts in human-machine dialogues. To investigate the effectiveness of our method, we train our model on the well-known Switchboard human-human dialogue dataset, and fine-tune it for predicting dialogue acts in human-machine conversation data, collected as part of the Amazon Alexa Prize 2018 competition. The results show that the CDAC model outperforms an utterance-level state of the art baseline by 8.0% on the Switchboard dataset, and is comparable to the latest reported state-of-the-art contextual DA classification results. Furthermore, our results show that fine-tuning the CDAC model on a small sample of manually labeled human-machine conversations allows CDAC to more accurately predict dialogue acts in real users' conversations, suggesting a promising direction for future improvements.
Integrative Task-Based Learning: Developing Speaking Skill and Increase Motivation via Instagram

The ability to communicate fluently and accurately is becoming one of the most important components of language elements in Malaysia.Some of the factors that cause to the inability to speak eloquently are low self-confidence, inadequate practices and less exposure to the language.Henceforth, this action research aimed to discover the potential use of integrating Instagram features and task-based learning activities to develop speaking skills and identify the level of pupils' motivation.In this study, an observation and semi structured oral interview were conducted as methods for data collection.Hence, eight pupils from an urban pre-school in Selangor and low level of English proficiency in a rural primary school in Negeri Sembilan, Malaysia were selected.The findings from the action research indicated that a majority of the pupils acknowledged that Instagram motivates and boosts their interest to practice English speaking and at the same time improve their speaking skills.It is concluded that incorporating of Instagram and task-based learning activities are aligned with the needs of 21 st Century learning and teaching strategies together with the potential to engage and motivate pupils in developing their speaking skills.
Deep Bayesian Self-Training

Supervised deep learning has been highly successful in recent years, achieving state-of-the-art results in most tasks. However, with the ongoing uptake of such methods in industrial applications, the requirement for large amounts of annotated data is often a challenge. In most real-world problems, manual annotation is practically intractable due to time/labour constraints; thus, the development of automated and adaptive data annotation systems is highly sought after. In this paper, we propose both a (1) deep Bayesian self-training methodology for automatic data annotation, by leveraging predictive uncertainty estimates using variational inference and modern neural network (NN) architectures, as well as (2) a practical adaptation procedure for handling high label variability between different dataset distributions through clustering of NN latent variable representations. An experimental study on both public and private datasets is presented illustrating the superior performance of the proposed approach over standard self-training baselines, highlighting the importance of predictive uncertainty estimates in safety-critical domains.
Continual Occlusion and Optical Flow Estimation

Two optical flow estimation problems are addressed: (i) occlusion estimation and handling, and (ii) estimation from image sequences longer than two frames. The proposed ContinualFlow method estimates occlusions before flow, avoiding the use of flow corrupted by occlusions for their estimation. We show that providing occlusion masks as an additional input to flow estimation improves the standard performance metric by more than 25% on both KITTI and Sintel. As a second contribution, a novel method for incorporating information from past frames into flow estimation is introduced. The previous frame flow serves as an input to occlusion estimation and as a prior in occluded regions, i.e. those without visual correspondences. By continually using the previous frame flow, ContinualFlow performance improves further by 18% on KITTI and 7% on Sintel, achieving top performance on KITTI and Sintel.
Improving Distantly Supervised Relation Extraction with Neural Noise Converter and Conditional Optimal Selector

Distant supervised relation extraction has been successfully applied to large corpus with thousands of relations. However, the inevitable wrong labeling problem by distant supervision will hurt the performance of relation extraction. In this paper, we propose a method with neural noise converter to alleviate the impact of noisy data, and a conditional optimal selector to make proper prediction. Our noise converter learns the structured transition matrix on logit level and captures the property of distant supervised relation extraction dataset. The conditional optimal selector on the other hand helps to make proper prediction decision of an entity pair even if the group of sentences is overwhelmed by no-relation sentences. We conduct experiments on a widely used dataset and the results show significant improvement over competitive baseline methods.
Support and Invertibility in Domain-Invariant Representations

Learning domain-invariant representations has become a popular approach to unsupervised domain adaptation and is often justified by invoking a particular suite of theoretical results. We argue that there are two significant flaws in such arguments. First, the results in question hold only for a fixed representation and do not account for information lost in non-invertible transformations. Second, domain invariance is often a far too strict requirement and does not always lead to consistent estimation, even under strong and favorable assumptions. In this work, we give generalization bounds for unsupervised domain adaptation that hold for any representation function by acknowledging the cost of non-invertibility. In addition, we show that penalizing distance between densities is often wasteful and propose a bound based on measuring the extent to which the support of the source domain covers the target domain. We perform experiments on well-known benchmarks that illustrate the short-comings of current standard practice.
Active Contour Driven by Weighted Hybrid Signed Pressure Force for Image Segmentation

This study presents a novel active contour model (ACM) driven by weighted global and local region-based signed pressure force (SPF) to segment images in the presence of intensity inhomogeneity and noise. First, an adaptive weighted global region-based SPF (GRSPF) function as the driving centers is designed based on the global image information, which is based on the normalized global intensity to update the weights of the inner and outer regions of the curve during iterations. Second, by introducing the normalized absolute local intensity differences as the weighs of the inner and outer regions, an adaptive weighted local region-based SPF (LRSPF) function is similarly defined. Third, instead of setting a fixed force, a force propagation function is introduced to automatically balance the interior and exterior forces according to the image feature. Meanwhile, by combing the adaptive GWSPF and LWSPF functions, a weighted hybrid region-based SPF function is defined, which can improve the efficiency and accuracy of the proposed model. The experimental results on real images demonstrate that the proposed model is more robust than the popular region-based ACMs for segmenting images with intensity inhomogeneity and noise. The code is available at https://github.com/fangchj2002/WHRSPF.
MLRDA: A Multi-Task Semi-Supervised Learning Framework for Drug-Drug Interaction Prediction

Drug-drug interactions (DDIs) are a major cause of preventable hospitalizations and deaths. Recently, researchers in the AI community try to improve DDI prediction in two directions, incorporating multiple drug features to better model the pharmacodynamics and adopting multi-task learning to exploit associations among DDI types. However, these two directions are challenging to reconcile due to the sparse nature of the DDI labels which inflates the risk of overfitting of multi-task learning models when incorporating multiple drug features. In this paper, we propose a multi-task semi-supervised learning framework MLRDA for DDI prediction. MLRDA effectively exploits information that is beneficial for DDI prediction in unlabeled drug data by leveraging a novel unsupervised disentangling loss CuXCov. The CuXCov loss cooperates with the classification loss to disentangle the DDI prediction relevant part from the irrelevant part in a representation learnt by an autoencoder, which helps to ease the difficulty in mining useful information for DDI prediction in both labeled and unlabeled drug data. Moreover, MLRDA adopts a multi-task learning framework to exploit associations among DDI types. Experimental results on real-world datasets demonstrate that MLRDA significantly outperforms state-of-the-art DDI prediction methods by up to 10.3% in AUPR.