Reviewerly/exHarmony huggingface dataset:
Adapting State-of-the-Art Deep Language Models to Clinical Information Extraction Systems: Potentials, Challenges, and Solutions

Deep learning (DL) has been widely used to solve problems with success in speech recognition, visual object recognition, and object detection for drug discovery and genomics. Natural language processing has achieved noticeable progress in artificial intelligence. This gives an opportunity to improve on the accuracy and human-computer interaction of clinical informatics. However, due to difference of vocabularies and context between a clinical environment and generic English, transplanting language models directly from up-to-date methods to real-world health care settings is not always satisfactory. Moreover, the legal restriction on using privacy-sensitive patient records hinders the progress in applying machine learning (ML) to clinical language processing.The aim of this study was to investigate 2 ways to adapt state-of-the-art language models to extracting patient information from free-form clinical narratives to populate a handover form at a nursing shift change automatically for proofing and revising by hand: first, by using domain-specific word representations and second, by using transfer learning models to adapt knowledge from general to clinical English. We have described the practical problem, composed it as an ML task known as information extraction, proposed methods for solving the task, and evaluated their performance.First, word representations trained from different domains served as the input of a DL system for information extraction. Second, the transfer learning model was applied as a way to adapt the knowledge learned from general text sources to the task domain. The goal was to gain improvements in the extraction performance, especially for the classes that were topically related but did not have a sufficient amount of model solutions available for ML directly from the target domain. A total of 3 independent datasets were generated for this task, and they were used as the training (101 patient reports), validation (100 patient reports), and test (100 patient reports) sets in our experiments.Our system is now the state-of-the-art in this task. Domain-specific word representations improved the macroaveraged F1 by 3.4%. Transferring the knowledge from general English corpora to the task-specific domain contributed a further 7.1% improvement. The best performance in populating the handover form with 37 headings was the macroaveraged F1 of 41.6% and F1 of 81.1% for filtering out irrelevant information. Performance differences between this system and its baseline were statistically significant (P<.001; Wilcoxon test).To our knowledge, our study is the first attempt to transfer models from general deep models to specific tasks in health care and gain a significant improvement. As transfer learning shows its advantage over other methods, especially on classes with a limited amount of training data, less experts' time is needed to annotate data for ML, which may enable good results even in resource-poor domains.
Garment Detectives: Discovering Clothes and Its Genre in Consumer Photos

Clothing image analysis has shown its potential for use in a wide range of applications such as personalized clothing recommendation. Given a consumer photo, this paper addresses the problem of finding clothes and recognizing the genre of that clothes. This problem is very challenging due to large variations of uncontrolled realistic imaging conditions. To tackle these challenges, we formulate a novel framework by integrating local features of multimodality as the instances of the price-collecting Steiner tree (PCST) problem to discover clothing regions, and exploiting visual style elements to discover the clothing genre. The experimental results show that our fully automatic approach is effective to identify irregular shape of clothing region, and it significantly improves the accuracy of clothing genre recognition for images taken in unconstrained environment.
"Nothing Comes Before Profit"

Researchers in HCI and STS are increasingly interested in describing ethics and values relevant for design practice, including the formulation of methods to guide value application. However, little work has addressed ethical considerations as they emerge in everyday conversations about ethics in venues such as social media. In this late breaking work, we describe online conversations about a concept known as "asshole design" on Reddit, and the relationship of this concept to another practitioner-focused concept known as "dark patterns." We analyzed 1002 posts from the subreddit '/r/assholedesign' to identify the types of artifact being shared and the interaction purposes that were perceived to be manipulative or unethical as a type of "asshole design." We identified a subset of these posts relating to dark patterns, quantifying their occurrences using an existing dark patterns typology.
A hybrid of whale optimization and late acceptance hill climbing based imputation to enhance classification performance in electronic health records

Electronic health records (EHR) are a major source of information in biomedical informatics. Yet, missing values are prominent characteristics of EHR. Prediction on dataset with missing values results in inaccurate inferences. Nearest neighbour imputation based on lazy learning approach is a proven technique for missing data imputation and is recognized as one among the top ten data mining algorithms due to its simplicity and understandability. But its performance is deteriorated due to the curse of dimensionality as unimportant features are likely to dominate. We address this problem by proposing a novel approach for feature weighting based on a hybrid of metaheuristic whale optimization algorithm (WOA) and local search late acceptance hill climbing algorithm (LAHCA) on nearest neighbour imputation method. Our proposed approach Metaheuristic and Local Search based Feature Weighted Nearest Neighbour Imputation (kNN+LAHCAWOA) also learns different k values for different test points. Our approach is tested on benchmark EHR datasets with three proven classifiers Support Vector Machines(SVM), Random forest(RF) and Deep neural networks(DNN). The results prove that kNN+LAHCAWOA is an effective imputation strategy and aids in improving the classification performance when compared with its competitor methods.
Min-Max Correlation Clustering via MultiCut

Correlation clustering is a fundamental combinatorial optimization problem arising in many contexts and applications that has been the subject of dozens of papers in the literature. In this problem we are given a general weighted graph where each edge is labeled positive or negative. The goal is to obtain a partitioning (clustering) of the vertices that minimizes disagreements – weight of negative edges trapped inside a cluster plus positive edges between different clusters. Most of the papers on this topic mainly focus on minimizing total disagreement, a global objective for this problem. In this paper we study a cluster-wise objective function that asks to minimize the maximum number of disagreements of each cluster, which we call min-max correlation clustering. The min-max objective is a natural objective that respects the quality of every cluster. In this paper, we provide the first nontrivial approximation algorithm for this problem achieving an $$\mathcal {O}(\log n)$$ approximation for general weighted graphs. To do so, we also obtain a corresponding result for multicut where we wish to find a multicut solution while trying to minimize the total weight of cut edges on every component. The results are then further improved to obtain an $$\mathcal {O}(r^2)$$ -approximation for min-max correlation clustering and min-max multicut for graphs that exclude $$K_{r,r}$$ minors.
Subspace structural constraint-based discriminative feature learning via nonnegative low rank representation

Feature subspace learning plays a significant role in pattern recognition, and many efforts have been made to generate increasingly discriminative learning models. Recently, several discriminative feature learning methods based on a representation model have been proposed, which have not only attracted considerable attention but also achieved success in practical applications. Nevertheless, these methods for constructing the learning model simply depend on the class labels of the training instances and fail to consider the essential subspace structural information hidden in them. In this paper, we propose a robust feature subspace learning approach based on a low-rank representation. In our approach, the low-rank representation coefficients are considered as weights to construct the constraint item for feature learning, which can introduce a subspace structural similarity constraint in the proposed learning model for facilitating data adaptation and robustness. Moreover, by placing the subspace learning and low-rank representation into a unified framework, they can benefit each other during the iteration process to realize an overall optimum. To achieve extra discrimination, linear regression is also incorporated into our model to enforce the projection features around and close to their label-based centers. Furthermore, an iterative numerical scheme is designed to solve our proposed objective function and ensure convergence. Extensive experimental results obtained using several public image datasets demonstrate the advantages and effectiveness of our novel approach compared with those of the existing methods.
Rapid syntactic adaptation in self-paced reading: Detectable, but only with many participants.

Temporarily ambiguous sentences that are disambiguated in favor of a less preferred parse are read more slowly than their unambiguous counterparts. This slowdown is referred to as a garden path effect. Recent self-paced reading studies have found that this effect decreased over the course of the experiment as participants were exposed to such syntactically ambiguous sentences. This decrease in the magnitude of the effect has been interpreted as evidence that readers calibrate their expectations to the context; this minimizes their surprise when they encounter these initially unexpected syntactic structures. Such recalibration of syntactic expectations, referred to as syntactic adaptation, is only one possible explanation for the decrease in garden path effect, however; this decrease could also be driven instead by increased familiarity with the self-paced reading paradigm (task adaptation). The goal of this article is to adjudicate between these two explanations. In a large between-group study (n = 642), we find evidence for syntactic adaptation over and above task adaptation. The magnitude of syntactic adaptation compared to task adaptation is very small, however. Power analyses show that a large number of participants is required to detect, with adequate power, syntactic adaptation in future between-subjects self-paced reading studies. This issue is exacerbated in experiments designed to detect modulations of the basic syntactic adaptation effect; such experiments are likely to be underpowered even with more than 1,200 participants. We conclude that while, contrary to recent suggestions, syntactic adaptation can be detected using self-paced reading, this paradigm is not very effective for studying this phenomenon. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Improved artificial bee colony algorithm based on escaped foraging strategy

This paper presents an improved artificial bee colony algorithm. Under the framework of the basic artificial bee colony algorithm, this paper redefines the artificial bee colony and introduces search strategies for group escape and foraging based on Levy flight. The proposed algorithm is named artificial bee colony algorithm based on escaped foraging strategy (EFSABC).There are different strategies for scout bees, onlookers, and free bees searching for honey sources in the EFSABC: all working bees relinquish old honey sources due to disturbance, and select different routines to seek new honey sources. Sixteen typical high-dimensional standard functions are used to verify the effectiveness of the proposed algorithm. The EFSABC algorithm outperforms the traditional artificial bee colony algorithm in all aspects.
Sampling uncertainty versus method uncertainty: A general framework with applications to omics biomarker selection

Abstract Uncertainty is a crucial issue in statistics which can be considered from different points of view. One type of uncertainty, typically referred to as sampling uncertainty, arises through the variability of results obtained when the same analysis strategy is applied to different samples. Another type of uncertainty arises through the variability of results obtained when using the same sample but different analysis strategies addressing the same research question. We denote this latter type of uncertainty as method uncertainty. It results from all the choices to be made for an analysis, for example, decisions related to data preparation, method choice, or model selection. In medical sciences, a large part of omics research is focused on the identification of molecular biomarkers, which can either be performed through ranking or by selection from among a large number of candidates. In this paper, we introduce a general resampling‐based framework to quantify and compare sampling and method uncertainty. For illustration, we apply this framework to different scenarios related to the selection and ranking of omics biomarkers in the context of acute myeloid leukemia: variable selection in multivariable regression using different types of omics markers, the ranking of biomarkers according to their predictive performance, and the identification of differentially expressed genes from RNA‐seq data. For all three scenarios, our findings suggest highly unstable results when the same analysis strategy is applied to two independent samples, indicating high sampling uncertainty and a comparatively smaller, but non‐negligible method uncertainty, which strongly depends on the methods being compared.
Designing Smart-Contract Based Auctions

In this paper, we developed an online auction system based on Ethereum smart contracts. A smart contract is executable code that runs on top of the blockchain to facilitate, execute and enforce an agreement between untrusted parties without the involvement of a trusted third party. A decentralised auction guarantees greater transparency and avoids cheating auctioneers. Since in Ethereum computation is expensive as transactions are executed and verified by all the nodes on Ethereum network, we analysed our implementation in terms of cost and time efficiency, obtaining promising results.
Emotion Recognition Using Cloud Model

Emotions often facilitate interactions among human beings, but the big variation of human emotional states make a negative effect on the reliable emotion recognition. We propose a novel algorithm to extract common features for each type of emotional states which can reliably present human emotions. To uncover the common features from uncertain emotional states, the backward cloud generator is used to discover {Ex, En, He} by integrating randomness and fuzziness. Finally, the proposed method for emotion recognition is verified on the common facial expression datasets, the Extended Cohn-Kanade (CK+) dataset and the Japanese female facial expression (JAFFE). The results are satisfactory, which shows cloud model is potentially useful in pattern recognition and machines learning.
Adaptive Detection With Constant False Alarm Ratio in a Non-Gaussian Noise Background

A class of adaptive detectors with constant false alarm ratio (CFAR) for weak signals' detection in additive non-Gaussian noise background is investigated. Although locally optimal detector (LOD) has the optimum detection performance, there are some disadvantages, such as complicated detection structure, poor adaptability, and difficulties in achieving CFAR in a time-varying noise background. In order to solve this problem, a cumulative distribution detector (CDD) is first proposed. However, CDD requires an accurate estimation of the cumulative distribution function for the non-Gaussian background, which limits its application. Therefore, a detector based on sigmoid function (SGD) is described. The performance of SGD is analyzed in detail, and then, an optimal SGD is obtained. In addition, by taking a simple approximation of that, a CFAR detector based on SGD is finally obtained, and the simulation results show that it has similar detection performance compared with LOD but provides relatively high efficiency and adaptability.
Efficient Strategy Selection for Moving Target Defense Under Multiple Attacks

In a real network environment, multiple types of attacks can occur. The more important the service or network, the more attacks it may suffer simultaneously. Moving target defense (MTD) technology is a revolutionary game-changing cyberspace technology that has found various applications in recent years. However, the existing strategies are targeted at defending against specific types of attacks and do not meet the security requirements for multiple attacks. Therefore, we propose a joint defense strategy based on the MTD that can select one or multiple mutant elements to defend against different types of attacks. In addition, we use the analytic hierarchy process (AHP) to quantify the factors affecting the attack and defense costs. After comprehensively analyzing the effects of the different MTD technologies against different attacks, we propose an efficient strategy selection algorithm based on joint defense. Finally, we conduct experiments to evaluate the selection of a joint defense strategy under multiple attacks. The experimental results demonstrate the feasibility and effectiveness of the proposed joint defense strategy selection approach.
Eye gaze behavior during affective picture viewing: Effects of motivational significance, gender, age, and repeated exposure

How top-down and bottom-up factors combine to determine eye movements during affective picture viewing is far from being completely understood. We investigated how observers’ fixation frequency and scanpath length – two indices of information seeking and intake – are related to self-reported valence (pleasantness) and arousal and depend on gender, age, and repeated exposure during affective picture viewing. We tracked the eye movements of 157 younger, middle-aged, and older adults when viewing 14 picture series each consisting of six thematically and affectively similar pictures. Participants’ valence and arousal ratings were registered for each series. Fixation frequency and scanpath length increased with self-rated unpleasantness and arousal and decreased across the six pictures within series. This decrease was age- and arousal-dependent. Compared to men, women exhibited a more exploratory scanning behavior. These findings suggest that observers’ affective appraisal, gender and age and repeated exposure to affective visual stimuli influence visual information seeking and intake.
EigenSent: Spectral sentence embeddings using higher-order Dynamic Mode Decomposition

Distributed representation of words, or word embeddings, have motivated methods for calculating semantic representations of word sequences such as phrases, sentences and paragraphs.Most of the existing methods to do so either use algorithms to learn such representations, or improve on calculating weighted averages of the word vectors.In this work, we experiment with spectral methods of signal representation and summarization as mechanisms for constructing such word-sequence embeddings in an unsupervised fashion.In particular, we explore an algorithm rooted in fluid-dynamics, known as higher-order Dynamic Mode Decomposition, which is designed to capture the eigenfrequencies, and hence the fundamental transition dynamics, of periodic and quasi-periodic systems.It is empirically observed that this approach, which we call EigenSent, can summarize transitions in a sequence of words and generate an embedding that can represent well the sequence itself.To the best of the authors' knowledge, this is the first application of a spectral decomposition and signal summarization technique on text, to create sentence embeddings.We test the efficacy of this algorithm in creating sentence embeddings on three public datasets, where it performs appreciably well.Moreover it is also shown that, due to the positive combination of their complementary properties, concatenating the embeddings generated by EigenSent with simple word vector averaging achieves state-of-the-art results.
Heuristic Authorship Obfuscation

Authorship verification is the task of determining whether or not two texts were written by the same author.This paper deals with the adversary task, called authorship obfuscation: Preventing verification by altering a tobe-obfuscated text.We introduce an approach that (1) models writing style difference as the Jensen-Shannon distance between the character n-gram distributions of texts, and (2) manipulates an author's subconsciously encoded writing style in a sophisticated manner using heuristic search.To obfuscate, we explore the huge space of textual variants in order to find a paraphrased version of the to-be-obfuscated text that has a sufficient Jensen-Shannon distance at minimal costs in terms of text quality loss.We analyze, quantify, and illustrate the rationale of this approach, define paraphrasing operators, derive obfuscation thresholds, and develop an effective obfuscation framework.Our authorship obfuscation approach defeats state-of-the-art verification approaches, including unmasking and compression models, while keeping text changes at a minimum.
Budgeted Policy Learning for Task-Oriented Dialogue Systems

This paper presents a new approach that extends Deep Dyna-Q (DDQ) by incorporating a Budget-Conscious Scheduling (BCS) to best utilize a fixed, small amount of user interactions (budget) for learning task-oriented dialogue agents.BCS consists of (1) a Poissonbased global scheduler to allocate budget over different stages of training; (2) a controller to decide at each training step whether the agent is trained using real or simulated experiences;(3) a user goal sampling module to generate the experiences that are most effective for policy learning.Experiments on a movie-ticket booking task with simulated and real users show that our approach leads to significant improvements in success rate over the state-ofthe-art baselines given the fixed budget.
Improving Lemmatization of Non-Standard Languages with Joint Learning

Enrique Manjavacas, Ákos Kádár, Mike Kestemont. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.