Reviewerly/exHarmony huggingface dataset:
Parallel multi-agent real-coded genetic algorithm for large-scale black-box single-objective optimisation

There is an ongoing evolution involving a new approach to large-scale optimisations based on co-evolutionary searches using interacting heterogeneous agent-processes via the implementation of synchronised genetic algorithms with local populations. The individualisation of heuristic operators at the level of agent-processes that implement independent evolutionary searches facilitate the improved likelihood of obtaining the best solutions in the fastest time. Based on this property, a parallel multi-agent single-objective real-coded genetic algorithm for large-scale constrained black-box single-objective optimisations (LSOPs) is proposed. This facilitates the effective frequency exchange of the best potential decisions between interacting agent-processes with individual parameters, such as types of crossover and mutation operators with their own characteristics. We have improved the quality of both solutions and the time-efficiency of a multi-agent real-coded genetic algorithm (MA−RCGA). A novel framework was developed that represents the aggregation of MA−RCGA with simulation models by implementing a set of objective functions for real-world large-scale optimisation problems such as the simulation model of the ecological-economics system implemented in the AnyLogic tool.
Towards a Quantum-Inspired Binary Classifier

Machine Learning classification models learn the relation between input as features and output as a class in order to predict the class for the new given input.Several research works have demonstrated the effectiveness of machine learning algorithms but the state-of-the-art algorithms are based on the classical theories of probability and logic.Quantum Mechanics (QM) has already shown its effectiveness in many fields and researchers have proposed several interesting results which cannot be obtained through classical theory.In recent years, researchers have been trying to investigate whether the QM can help to improve the classical machine learning algorithms.It is believed that the theory of QM may also inspire an effective algorithm if it is implemented properly.From this inspiration, we propose the quantum-inspired binary classifier, which is based on quantum detection theory.We used text corpora and image corpora to explore the effect of our proposed model.Our proposed model outperforms the state-of-the-art models in terms of precision, recall, and F-measure for several topics (categories) in the 20 newsgroup text corpora.Our proposed model outperformed all the baselines in terms of recall when the MNIST handwritten image dataset was used; F-measure is also higher for most of the categories and precision is also higher for some categories.Our proposed model suggests that binary classification effectiveness can be achieved by using quantum detection theory.In particular, we found that our Quantum-Inspired Binary Classifier can increase the precision, recall, and F-measure of classification where the state-of-the-art methods cannot.
An efficient single document Arabic text summarization using a combination of statistical and semantic features

The exponential growth of online textual data triggered the crucial need for an effective and powerful tool that automatically provides the desired content in a summarized form while preserving core information. In this paper, we propose an automatic, generic, and extractive Arabic single document summarizing method aiming at producing a sufficiently informative summary. The proposed extractive method evaluates each sentence based on a combination of statistical and semantic features in which a novel formulation is used taking into account sentence importance, coverage and diversity. Further, two summarizing techniques including score-based and supervised machine learning were employed to produce the summary and then assist leveraging the designed features. We demonstrate the effectiveness of the proposed method through a set of experiments under EASC corpus using ROUGE measure. Compared to some existing related work, the experimental evaluation shows the strength of the proposed method in terms of precision, recall, and F-score performance metrics.
A Survey of Blockchain and Its Applications

Blockchain is one of the technologies which appeared in the last decade and brought a lot of promise with it. Much researches are being conducted actively to explore the full capabilities of Blockchain. Some believe that Blockchain is key for a decentralized society. Especially, we are considering blockchain as the security scheme to protect the privacies of the objects to be augmented in intelligent mobile augmented reality (IMAR) project. To do that, this paper describe an overview of an blockchain.
Detecting Examinees With Item Preknowledge in Large-Scale Testing Using Extreme Gradient Boosting (XGBoost)

Researchers frequently use machine-learning methods in many fields. In the area of detecting fraud in testing, there have been relatively few studies that have used these methods to identify potential testing fraud. In this study, a technical review of a recently developed state-of-the-art algorithm, Extreme Gradient Boosting (XGBoost), is provided and the utility of XGBoost in detecting examinees with potential item preknowledge is investigated using a real data set that includes examinees who engaged in fraudulent testing behavior, such as illegally obtaining live test content before the exam. Four different XGBoost models were trained using different sets of input features based on (a) only dichotomous item responses, (b) only nominal item responses, (c) both dichotomous item responses and response times, and (d) both nominal item responses and response times. The predictive performance of each model was evaluated using the area under the receiving operating characteristic curve and several classification measures such as the false-positive rate, true-positive rate, and precision. For comparison purposes, the results from two person-fit statistics on the same data set were also provided. The results indicated that XGBoost successfully classified the honest test takers and fraudulent test takers with item preknowledge. Particularly, the classification performance of XGBoost was reasonably good when the response time information and item responses were both taken into account.
Sensitivity analysis on initial classifier accuracy in fuzziness based semi-supervised learning

Semi-supervised learning can be described from different perspectives, which plays a crucial role in the study of machine learning. In this study, a new aspect of semi-supervised learning is explored by investigating the divide-and-conquer strategy based on fuzziness to improve the performance of classifiers. In such an approach, adding a category of samples with low fuzziness in the training set can improve the training accuracy, which is experimentally confirmed and explained in the theory of learning from noisy data. The significance of initial accuracy of a base classifier in improving classifier’s performance is further studied. It is observed that the initial accuracy of a base classifier has a significant impact on the improvement of classifier’s performance. Experimental results exhibit that the improvement of accuracy, which is sensitive to the base classifier, attains its maximum when the initial accuracy is between 70% and 80%.
Analyzing and interpreting neural networks for NLP: A report on the first BlackboxNLP workshop

Abstract The Empirical Methods in Natural Language Processing (EMNLP) 2018 workshop BlackboxNLP was dedicated to resources and techniques specifically developed for analyzing and understanding the inner-workings and representations acquired by neural models of language. Approaches included: systematic manipulation of input to neural networks and investigating the impact on their performance, testing whether interpretable knowledge can be decoded from intermediate representations acquired by neural networks, proposing modifications to neural network architectures to make their knowledge state or generated output more explainable, and examining the performance of networks on simplified or formal languages. Here we review a number of representative studies in each category.
Framework for syntactic string similarity measures

Abstract Similarity measure is an essential component of information retrieval, document clustering, text summarization, and question answering, among others. In this paper, we introduce a general framework of syntactic similarity measures for matching short text. We thoroughly analyze the measures by dividing them into three components: character-level similarity, string segmentation, and matching technique. Soft variants of the measures are also introduced. With the help of two existing toolkits (SecondString and SimMetric), we provide an open-source Java toolkit of the proposed framework, which integrates the individual components together so that completely new combinations can be created. Experimental results reveal that the performance of the similarity measures depends on the type of the dataset. For well-maintained dataset, using a token-level measure is important but the basic (crisp) variant is usually enough. For uncontrolled dataset where typing errors are expected, the soft variants of the token-level measures are necessary. Among all tested measures, a soft token-level measure that combines set matching and q-grams at the character level perform best. A gap between human perception and syntactic measures still remains due to lacking semantic analysis.
Autonomous robotic exploration based on frontier point optimization and multistep path planning

Autonomous robotic exploration of an unknown environment is a key technology for robot intelligence. In order to improve the efficiency of it, we propose a strategy based on frontier point optimization and multistep path planning in this paper. In the frontier points' optimization section, we present a random frontier points' optimization (RFPO) algorithm to select the frontier point with the highest evaluation value as the target frontier point. The evaluation function of frontier points is defined by considering information gain, navigation cost, and the precision of the localization of the robots. In the path planning section, we propose a multistep exploration strategy. Instead of planning the global path from the current position of the robot to the target frontier point directly, we set a local exploration path step size. When the robot's movement distance reaches the local exploration path step size, we reselect the current optimal frontier point for path planning to reduce the possibility that the robot may take some repetitive paths. Finally, the relevant experiments are carried out to verify the effectiveness of this strategy.
Deceptive consumer review detection: a survey

Consumer reviews are considered to be of utmost significance in the field of e-commerce, for they have a stronghold in deciding the revenue of a business. When arriving at a purchasing decision, a majority of online consumers rely on reviews since they offer credible means of mining opinions of other consumers regarding a particular product. The trustworthiness of online reviews directly affects a company's reputation and profitability, which is why certain business owners pay fraudsters to generate deceptive reviews. Such generation of deceptive reviews which manipulate the purchasing decision of consumers is a persistent and harmful issue. Hence, developing methods to assist businesses and consumers by distinguishing between credible reviews and deceptive reviews remains to be a crucial, yet challenging task. In view of that, this paper unravels prominent techniques that have been proposed to solve the issue of deceptive review detection. Accordingly, the primary goal of this paper is to provide an in-depth analysis of current research on detecting deceptive reviews and to identify the characteristics, strengths, and bottlenecks of those methodologies which may need further improvements.
Grey Wolf Optimizer: Theory, Literature Review, and Application in Computational Fluid Dynamics Problems

This chapter first discusses inspirations, methematicam models, and an in-depth literature of the recently proposed Grey Wolf Optimizer (GWO). Then, several experiments are conducted to analyze and benchmark the performance of different variants and improvements of this algorithm. The chapter also investigates the application of the GWO variants in finding an optimal design for a ship propeller.
Methodology and case study of quantitative preliminary hazard analysis based on cloud model

Preliminary hazard analysis (PHA) is an effective method to identify and evaluate hazards in a system. Then, prevention measures can be set according to assessment results. However, assessment results may not be detailed and reasonable enough due to the qualitative assessment by PHA. This paper proposes a quantitative PHA called cloud model-preliminary hazard analysis (CM-PHA). The cloud model (CM) is utilized to improve PHA. The cloud model-hazard grade (CM-HG) is proposed to classify hazards, and the algorithm of CM-HG is established. Then, steps to conduct quantitative assessments of hazards are provided. The process of CM-PHA to obtain the CM-PHA diagram is worked out accordingly. A case study of hazards in the biomass gasification station is developed based on CM-PHA. A qualitative PHA is introduced and compared with the proposed method. Comparison results show that the assessment results obtained by CM-PHA are more detailed and precise, and corresponding prevention measures for hazards are more reasonable.
Inverse reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units

Reinforcement learning (RL) provides a promising technique to solve complex sequential decision making problems in health care domains. To ensure such applications, an explicit reward function encoding domain knowledge should be specified beforehand to indicate the goal of tasks. However, there is usually no explicit information regarding the reward function in medical records. It is then necessary to consider an approach whereby the reward function can be learned from a set of presumably optimal treatment trajectories using retrospective real medical data. This paper applies inverse RL in inferring the reward functions that clinicians have in mind during their decisions on weaning of mechanical ventilation and sedative dosing in Intensive Care Units (ICUs).We model the decision making problem as a Markov Decision Process, and use a batch RL method, Fitted Q Iterations with Gradient Boosting Decision Tree, to learn a suitable ventilator weaning policy from real trajectories in retrospective ICU data. A Bayesian inverse RL method is then applied to infer the latent reward functions in terms of weights in trading off various aspects of evaluation criterion. We then evaluate how the policy learned using the Bayesian inverse RL method matches the policy given by clinicians, as compared to other policies learned with fixed reward functions.Results show that the inverse RL method is capable of extracting meaningful indicators for recommending extubation readiness and sedative dosage, indicating that clinicians pay more attention to patients' physiological stability (e.g., heart rate and respiration rate), rather than oxygenation criteria (FiO2, PEEP and SpO2) which is supported by previous RL methods. Moreover, by discovering the optimal weights, new effective treatment protocols can be suggested.Inverse RL is an effective approach to discovering clinicians' underlying reward functions for designing better treatment protocols in the ventilation weaning and sedative dosing in future ICUs.
Seismic Random Noise Attenuation Using Sparse Low-Rank Estimation of the Signal in the Time–Frequency Domain

Suppression of random noise in seismic data is a challenging preprocessing task. We propose a new denoising method, which includes the following steps. First, the short-time Fourier transform of the noisy seismic signal is computed. Then, a sparse low-rank matrix is estimated based on solving a nonconvex penalty function. A threshold function associated with the penalty function based on the optimal shrinkage of the singular values is employed to solve the penalty function. The nonconvex penalty function induces sparsity to the time-frequency (TF) matrix, and the optimal shrinkage optimizes the thresholding function to extract the singular values. Finally, the seismic signal is synthesized based on the estimated sparse low-rank TF matrix. We evaluate the proposed method by using a synthetic seismic section contaminated by random noise and a prestack real seismic dataset from an oil field in the southwest of Iran. The synchrosqueezed wavelet transform-OptShrink, synchrosqueezed wavelet transform-Go Decomposition, and classical f-x singular spectrum analysis methods are selected to compare the performance of the proposed method. The results indicate the superiority of the proposed method compared to the other state-of-the-art noise suppression methods.
Electronic medical record security sharing model based on blockchain

The emergence of electronic medical records has provided great convenience for the storage and analysis of medical data. However, electronic medical records contain a large amount of personal privacy information, it is still very difficult to share medical information among various medical institutions. As the underlying technology of Bitcoin, blockchain technology has the characteristics of decentralization, security, trustworthiness, collective maintenance, and cannot be tampered, it is suitable for data protection and sharing. In this paper, data masking technology and Inter Planetary File System (IPFS) are introduced to build a safe and efficient electronic medical record sharing model based on blockchain. The model can not only guarantee the security of medical data, but also save resources in blockchain.
Kyub

We present an interactive editing system for laser cutting called kyub. Kyub allows users to create models efficiently in 3D, which it then unfolds into the 2D plates laser cutters expect. Unlike earlier systems, such as FlatFitFab, kyub affords construction based on closed box structures, which allows users to turn very thin material, such as 4mm plywood, into objects capable of withstanding large forces, such as chairs users can actually sit on. To afford such sturdy construction, every kyub project begins with a simple finger-joint "boxel"-a structure we found to be capable of withstanding over 500kg of load. Users then extend their model by attaching additional boxels. Boxels merge automatically, resulting in larger, yet equally strong structures. While the concept of stacking boxels allows kyub to offer the strong affordance and ease of use of a voxel-based editor, boxels are not confined to a grid and readily combine with kuyb's various geometry deformation tools. In our technical evaluation, objects built with kyub withstood hundreds of kilograms of loads. In our user study, non-engineers rated the learnability of kyub 6.1/7.
The Adventures of Older Authors

This paper presents co-design fiction as an approach to engaging users in imagining, envisioning and speculating not just on future technology but future life through co-created fictional works. Design fiction in research is often created or written by researchers. There is relatively little critical discussion of how to co-create design fictions with end-users, with the concomitant opportunities and challenges this poses. To fill this gap in knowledge, we conducted co-design fiction workshops with nine older creative writers, utilising prompts to inspire discussion and engage their imaginative writing about the trend towards tracking and monitoring older people. Their stories revealed futures of neither dystopia nor utopia but of social and moral dilemmas narrating their wish not just to "maintain their independence", but a palpable desire for adventure and very nuanced senses of how they wish to take control. We discuss inherent tensions in the control of the co-design fiction process; balancing the author's need for freedom and creativity with the researcher's desire to guide the process toward the design investigation at hand.
Word n-gram attention models for sentence similarity and inference

Semantic Textual Similarity and Natural Language Inference are two popular natural language understanding tasks used to benchmark sentence representation models where two sentences are paired. In such tasks sentences are represented as bag of words, sequences, trees or convolutions, but the attention model is based on word pairs. In this article we introduce the use of word n-grams in the attention model. Our results on five datasets show an error reduction of up to 41% with respect to the word-based attention model. The improvements are especially relevant with low data regimes and, in the case of natural language inference, on the recently released hard subset of Natural Language Inference datasets.
Abstract Text Summarization with a Convolutional Seq2seq Model

Abstract text summarization aims to offer a highly condensed and valuable information that expresses the main ideas of the text. Most previous researches focus on extractive models. In this work, we put forward a new generative model based on convolutional seq2seq architecture. A hierarchical CNN framework is much more efficient than the conventional RNN seq2seq models. We also equip our model with a copying mechanism to deal with the rare or unseen words. Additionally, we incorporate a hierarchical attention mechanism to model the keywords and key sentences simultaneously. Finally we verify our model on two real-life datasets, GigaWord and DUC corpus. The experiment results verify the effectiveness of our model as it outperforms state-of-the-art alternatives consistently and statistical significantly.
Investigating Implicit Gender Bias and Embodiment of White Males in Virtual Reality with Full Body Visuomotor Synchrony

Previous research has shown that when White people embody a black avatar in virtual reality (VR) with full body visuomotor synchrony, this can reduce their implicit racial bias. In this paper, we put men in female and male avatars in VR with full visuomotor synchrony using wearable trackers and investigated implicit gender bias and embodiment. We found that participants embodied in female avatars displayed significantly higher levels of implicit gender bias than those embodied in male avatars. The implicit gender bias actually increased after exposure to female embodiment in contrast to male embodiment. Results also showed that participants felt embodied in their avatars regardless of gender matching, demonstrating that wearable trackers can be used for a realistic sense of avatar embodiment in VR. We discuss the future implications of these findings for both VR scenarios and embodiment technologies.