Reviewerly/exHarmony huggingface dataset:
Team yeon-zi at SemEval-2019 Task 4: Hyperpartisan News Detection by De-noising Weakly-labeled Data

This paper describes our system submitted to SemEval-2019 Task 4: Hyperpartisan News Detection.We focus on removing the inherent noise in the hyperpartisanship dataset from both data-level and model-level by leveraging semi-supervised pseudo-labels and the stateof-the-art BERT model.Our model achieves 75.8% accuracy in the final by-article dataset without ensemble learning.
Vessel Optimal Transport for Automated Alignment of Retinal Fundus Images

Optimal transport has emerged as a promising and useful tool for supporting modern image processing applications such as medical imaging and scientific visualization. Indeed, the optimal transport theory enables great flexibility in modeling problems related to image registration, as different optimization resources can be successfully used as well as the choice of suitable matching models to align the images. In this paper, we introduce an automated framework for fundus image registration which unifies optimal transport theory, image processing tools, and graph matching schemes into a functional and concise methodology. Given two ocular fundus images, we construct representative graphs which embed in their structures spatial and topological information from the eye's blood vessels. The graphs produced are then used as input by our optimal transport model in order to establish a correspondence between their sets of nodes. Finally, geometric transformations are performed between the images so as to accomplish the registration task properly. Our formulation relies on the solid mathematical foundation of optimal transport as a constrained optimization problem, being also robust when dealing with outliers created during the matching stage. We demonstrate the accuracy and effectiveness of the present framework throughout a comprehensive set of qualitative and quantitative comparisons against several influential state-of-the-art methods on various fundus image databases.
Bayesian Personalized Feature Interaction Selection for Factorization Machines

Factorization Machines (FMs) are widely used for feature-based collaborative filtering tasks, as they are very effective at modeling feature interactions. Existing FM-based methods usually take all feature interactions into account, which is unreasonable because not all feature interactions are helpful: incorporating useless feature interactions will introduce noise and degrade the recommendation performance. Recently, methods that perform Feature Interaction Selection (FIS) have attracted attention because of their effectiveness at filtering out useless feature interactions. However, they assume that all users share the same feature interactions, which is not necessarily true, especially for collaborative filtering tasks. In this work, we address this issue and study Personalized Feature Interaction Selection (P-FIS) by proposing a Bayesian Personalized Feature Interaction Selection (BP-FIS) mechanism under the Bayesian Variable Selection (BVS) theory. Specifically, we first introduce interaction selection variables with hereditary spike and slab priors for P-FIS. Then, we form a Bayesian generative model and derive the Evidence Lower Bound (ELBO), which can be optimized by an efficient Stochastic Gradient Variational Bayes (SGVB) method to learn the parameters. Finally, because BP-FIS can be seamlessly integrated with different variants of FMs, we implement two FM variants under the proposed BP-FIS. We carry out experiments on three benchmark datasets. The empirical results demonstrate the effectiveness of BP-FIS for selecting personalized interactions and improving the recommendation performance.
Indentation: Simply a Matter of Style or Support for Program Comprehension?

An early study showed that indentation is not a matter of style, but provides actual support for program comprehension. In this paper, we present a non-exact replication of this study. Our aim is to provide empirical evidence for the suggested level of indentation made by many style guides. Following Miara and others, we also included the perceived difficulty, and we extended the original design to gain additional insights into the influence of indentation on visual effort by employing an eye-tracker. In the course of our study, we asked 22 participants to calculate the output of Java code snippets with different levels of indentation, while we recorded their gaze behavior. We did not find any indication that the indentation levels affect program comprehension or visual effort, so we could not replicate the findings of Miara and others. Nevertheless, our modernization of the original experiment design is a promising starting point for future studies in this field.
Identifying vulnerable older adult populations by contextualizing geriatric syndrome information in clinical notes of electronic health records

Abstract Objective Geriatric syndromes such as functional disability and lack of social support are often not encoded in electronic health records (EHRs), thus obscuring the identification of vulnerable older adults in need of additional medical and social services. In this study, we automatically identify vulnerable older adult patients with geriatric syndrome based on clinical notes extracted from an EHR system, and demonstrate how contextual information can improve the process. Materials and Methods We propose a novel end-to-end neural architecture to identify sentences that contain geriatric syndromes. Our model learns a representation of the sentence and augments it with contextual information: surrounding sentences, the entire clinical document, and the diagnosis codes associated with the document. We trained our system on annotated notes from 85 patients, tuned the model on another 50 patients, and evaluated its performance on the rest, 50 patients. Results Contextual information improved classification, with the most effective context coming from the surrounding sentences. At sentence level, our best performing model achieved a micro-F1 of 0.605, significantly outperforming context-free baselines. At patient level, our best model achieved a micro-F1 of 0.843. Discussion Our solution can be used to expand the identification of vulnerable older adults with geriatric syndromes. Since functional and social factors are often not captured by diagnosis codes in EHRs, the automatic identification of the geriatric syndrome can reduce disparities by ensuring consistent care across the older adult population. Conclusion EHR free-text can be used to identify vulnerable older adults with a range of geriatric syndromes.
REVISITING THE SPANISH ELICITED IMITATION TASK

Abstract Elicited imitation tasks (EITs) have been shown to be a valid, reliable, and practical method for establishing second-language (L2) oral proficiency across a variety of languages. Nevertheless, research that has validated existing EITs has most often explored a narrow range of learner proficiencies or has examined how well the EIT distinguishes between low- and high-level learners. Bowden’s (2016) validation study of the Spanish EIT was the first to expand the range of learner proficiencies by including low, advanced, and very advanced L2 learners; her results suggested that, in its current state, the EIT may not be equipped to distinguish finer-grained levels of proficiency, especially at more advanced levels. The present study revisits the Spanish EIT to examine (a) its ability to distinguish between higher level learners and (b) whether including additional, more challenging EIT items allows for finer-grained distinctions at higher levels. Results suggest that the addition of longer, more challenging EIT items can improve the discriminability of the test among more advanced L2 learners.
Identification of Darknet Markets’ Bitcoin Addresses by Voting Per-address Classification Results

Bitcoin is a decentralized digital currency whose transactions are recorded in a common ledger, so called blockchain. Due to the anonymity and lack of law enforcement, Bitcoin has been misused in darknet markets which deal with illegal products, such as drugs and weapons. Therefore from the security forensics aspect, it is demanded to establish an approach to identify newly emerged darknet markets' transactions and addresses. In this paper, we thoroughly analyze Bitcoin transactions and addresses related to darknet markets and propose a novel identification method of darknet markets' addresses. To improve the identification performance, we propose a voting based method which decides the labels of multiple addresses controlled by the same user based on the number of the majority label. Through the computer simulation with more than 200K Bitcoin addresses, it was shown that our voting based method outperforms the nonvoting based one in terms of precision, recal, and F1 score. We also found that DNM's addresses pay higher fees than others, which significantly improves the classification.
BlockSIM: A practical simulation tool for optimal network design, stability and planning

As the benefits of blockchain become clear, in the past year we have witnessed an explosive growth in the use of blockchain based systems. However, as with any new technology, adoption of blockchain carries with it, as yet not widely known, risks that should be properly evaluated and understood prior to design and development. Given the purely distributed nature of blockchain, in which nodes can span continents, a key aspect in planning and designing blockchain systems which is not readily incorporated in a modern architect’s vernacular is consideration of eventual consistency, system stability and scalability when designing blockchain based systems. This is compounded by the fact that, as yet, very few practical tools are available to help architects better understand and plan for the above. In this paper we introduce BlockSIM, a comprehensive and open source blockchain system simulation tool which can assist blockchain architects better evaluate the performance of planned private blockchain networks by running scenarios and decide the optimal system parameters suited for their purposes. We compare the results of our simulation with real blockchain networks and demonstrate that BlockSIM can be used effectively by architects of blockchain systems to plan and implement scalable, stable and resilient blockchain networks. Finally, we demonstrate via a real life example how architects can apply BlockSIM to plan and design real-world blockchain systems.
Research of Bearing Fault Diagnosis Method Based on Multi-Layer Extreme Learning Machine Optimized by Novel Ant Lion Algorithm

In this paper, a novel bearing fault diagnosis method based on multi-layer extreme learning machine (MELM) optimized by the novel ant lion algorithm (NALO) is proposed. First, using permutation entropy of different scales (MPE) to extract fault features of bearings, a group of fault feature vectors composed of permutation entropy is obtained. Then, the fault feature vectors are classified by the MELM. However, with the increase of the number of hidden layers, the random input weight and bias will also increase, which will lead to the increase of the randomness of the MELM and affect the accuracy of fault diagnosis. Therefore, this paper uses the NALO to optimize the MELM. For the NALO, opposite populations are added to the initial population to improve its global search ability. When the ant lion updates its location, the influence of pheromones left by other ants with a certain sensing distance is taken into account to prevent the ant lion from falling into the local optimal and increased the robustness. Finally, the NALO-MELM and other bearing fault diagnosis methods are applied to the bearing fault experiment of Western Reserve University to test the performance and generalization of the proposed method.
Breaking the Cornucopian Paradigm

The Internet and digital devices are increasingly embedded in our everyday lives. The hidden environmental impacts of this infrastructure are substantial and quietly growing at an increasing rate. Our collective Internet use is following a 'Cornucopian paradigm', which is unsustainable. And yet, while intentionally limiting our online connectivity might be seen negatively as a retrograde step, in this paper, we offer ways in which users might welcome attempts to moderate their Internet use through improving four aspects of our digitally-mediated lives: relationships, digital wellbeing, productivity at work, and online privacy. Given these areas, we discuss how our research agenda may realistically be facilitated and what challenges we may face in moving from the reinforcement of 'business as usual' trends. By investigating and developing user-centred, moderate Internet use, we can 'break' the Cornucopian paradigm.
Recurrent neural networks for hydrodynamic imaging using a 2D-sensitive artificial lateral line

The lateral line is a mechanosensory organ found in fish and amphibians that allows them to sense and act on their near-field hydrodynamic environment. We present a 2D-sensitive artificial lateral line (ALL) comprising eight all-optical flow sensors, which we use to measure hydrodynamic velocity profiles along the sensor array in response to a moving object in its vicinity. We then use the measured velocity profiles to reconstruct the object's location, via two types of neural networks: feed-forward and recurrent. Several implementations of feed-forward neural networks for ALL source localisation exist, while recurrent neural networks may be more appropriate for this task. The performance of a recurrent neural network (the long short-term memory, LSTM) is compared to that of a feed-forward neural network (the online-sequential extreme learning machine, OS-ELM) via localizing a 6 cm sphere moving at 13 cm s-1. Results show that, in a 62 cm [Formula: see text] 9.5 cm area of interest, the LSTM outperforms the OS-ELM with an average localisation error of 0.72 cm compared to 4.27 cm, respectively. Furthermore, the recurrent network is relatively less affected by noise, indicating that recurrent connections can be beneficial for hydrodynamic object localisation.
Hidden fuzzy information: Requirement specification and measurement of project provider performance using the best worst method

The requirement specification process is an important part of a project and has the potential to prevent problems that may last for years after a project is delivered. Previous studies on the requirement specification process have focused on clarifying stated fuzzy terms in software requirement engineering. However, in many projects there is information that is not stated, but it is implied and can be inferred. This hidden information is usually ignored due to the assumption that 'the provider understands what they mean/need'. This assumption is not always true. Such information, if extracted, may include fuzzy terms, namely hidden fuzzy terms (HFTs), which need specification. Therefore, these fuzzy terms have to be identified and then specified to avoid potential future consequences. This study proposes an algorithm to extract the hidden fuzzy terms, utilises a fuzzy inference system (FIS) to specify them, and applies the best worst multi-criteria decision making method (BWM) to evaluate the delivered product and measure the performance of the provider. The model is then used to examine a case from Defence Housing Australia. Such evaluation and measurement enable the project owner/manager to have a transparent basis to support decisions later in different phases of the project, and to ultimately reduce the likelihood of conflict and the receipt of an unsatisfactory product.
Improving Parameter Estimation of Entropic Uncertainty Relation in Continuous-Variable Quantum Key Distribution

The entropic uncertainty relation (EUR) is of significant importance in the security proof of continuous-variable quantum key distribution under coherent attacks. The parameter estimation in the EUR method contains the estimation of the covariance matrix (CM), as well as the max-entropy. The discussions in previous works have not involved the effect of finite-size on estimating the CM, which will further affect the estimation of leakage information. In this work, we address this issue by adapting the parameter estimation technique to the EUR analysis method under composable security frameworks. We also use the double-data modulation method to improve the parameter estimation step, where all the states can be exploited for both parameter estimation and key generation; thus, the statistical fluctuation of estimating the max-entropy disappears. The result shows that the adapted method can effectively estimate parameters in EUR analysis. Moreover, the double-data modulation method can, to a large extent, save the key consumption, which further improves the performance in practical implementations of the EUR.
Graph-based multimodal fusion with metric learning for multimodal classification

In this paper, a graph-based, supervised classification method for multimodal data is introduced. It can be applied on data of any type consisting of any number of modalities and can also be used for the classification of datasets with missing modalities. The proposed method maps the features extracted from every modality to a space where the intrinsic structure of the multimodal data is kept. In order to map the extracted features of the different modalities into the same space and, at the same time, maintain the feature distances between similar and dissimilar modality data instances, a metric learning method is used. The proposed method has been evaluated on NUS-Wide, NTU-RGBD and AV-Letters multimodal datasets and has shown competitive results with the state-of-the-art methods in the field, while is able to cope with datasets with missing modalities.
Mobile data-driven language learning: Affordances and learners’ perception

Data-driven learning (DDL) is a learner-focused approach which promotes language learners’ discovery of linguistic patterns of use and meaning by examining extensive samples of attested uses of language. Despite the emergence of mobile-assisted language learning (MALL) and its affordances, i.e. individualization and personalization, the potential of DDL in this context has not been widely explored. This study involved the creation of a mobile language learning app based on freely available natural language processing (NLP) tools, followed by a test of the app to gather the attitudes and perceptions of several groups of language learners across Europe. The results suggest a generally positive evaluation of DDL’s instant and personalized feedback and direct access to a variety of tools. Besides, suggestions for improvement were made concerning the design of the tasks, such as the addition of further built-in tools and adaptations to hardware constraints. Analyses also showed a need for specialized learner training, so as to grasp the potential of the feedback provided. This study may be construed as a first step towards creating more fleshed-out tools and further investigating the potential of combining DDL and MALL.
An Improved Approach Based on CNN-RNNs for Mathematical Expression Recognition

Mathematical expression recognition (MER) in images is a challenging task due to formula symbol recognition and structured analysis. Optical character recognition (OCR) has been used in natural language recognition and many areas. However, it is difficult for OCR to recognize some special formula symbols and accurately confirm their positions in MER. In this paper, an improved end-to-end MER approach based on CNN-RNNs (convolutional neural network - recurrent neural networks) is proposed to optimize the processing for the formula symbol recognition and localization. In our proposed approach, we extract the mathematical expression features in the images by CNN and generate the mathematical expression by RNNs. In order to improve the acquisition of fuzzy or small symbolic feature information and get an accurate symbol position, we double the source images and extract image features with preprocessed images by CNN. And we add a double-attention mechanism between the encoder and decoder so that the symbol position can be obtained accurately. In addition, to prevent overfitting, a dropout layer is introduced to improve the generalization of the MER model. We do the experiment on IM2LATEX-100K dataset and obtain the comparative result. A BLEU accuracy of 88.42% is achieved, which is better than other methods.
Establishment of the Deep Mine Microbial Observatory (DeMMO), South Dakota, USA, a Geochemically Stable Portal Into the Deep Subsurface

Identifying temporal trends in deep subsurface geomicrobiology is challenging as it requires both in-depth knowledge of in situ geochemistry and innovative sampling techniques. Subsurface microbial dynamics can only be understood in the context of accompanying geochemistry, and thus, it is imperative to first characterize available microbial habitats and their temporal evolution. Also, samples must be acquired in a clean and consistent manner to avoid artefacts stemming from surface microbes, atmospheric contamination, or external temporal variability. To facilitate these ends, we established the Deep Mine Microbial Observatory (DeMMO) in the Sanford Underground Research Facility (SURF), Lead, SD, USA to sample naturally draining fracture fluids at six spatially distributed sites from the shallowest (800 ft) to the deepest accessible (4,850 ft) depths. Here we report on the installation and subsequent two-year aqueous geochemical monitoring campaign of the DeMMO network. DeMMO fluids have distinct geochemical compositions showing differences with respect to depth, proximity to mine workings, and host rock geology. Most measurements were remarkably stable through the two-year sampling window, illustrating temporal stability of the water sources to each site, including over induced perturbations such as drilling. Interestingly, there was a lack of seasonality even at shallowest sites, indicating limited direct communication with modern meteoric waters. Patterns of fluid geochemistry are distinct between sites, and largely predictable based upon our understanding of the lithology and inorganic geochemistry of the host rocks. Thermodynamic calculations suggest that both inorganic and organic redox reactions can yield energy to, respectively, lithotrophic and heterotrophic microorganisms in this system, although the yields vary considerably by site. We conclude that each DeMMO site represents a unique window into the deep subsurface of SURF, accessing distinct fluid pockets, aqueous geochemistry, and dissolved gas geochemistry – providing stable conditions that facilitate long-term habitation of subsurface fractures and water pockets by distinct microbial communities.
Innovative Research and Practice of Teachers’ Teaching Quality Evaluation under the Guidance of ‘Innovation and Entrepreneurship’

Under the guidance of the national 'double innovation' development strategy,some effective methods, such as updating concepts, improving the evaluation system, and changing supervision functions, introduction of diversified evaluation methods etc, have been adopted to address the problems existing in the evaluation of teachers' teaching quality in our school in order to promote teachers' self-improvement and self-improvement. The method of mathematical fuzzy level analysis has been used to implement the evaluation of teachers' teaching quality scientifically and objectively. It plays a positive role in promoting teachers' enthusiasm for teaching, improving the quality of education and teaching, and ensuring the training of qualified personnel in colleges and universities.