Reviewerly/exHarmony huggingface dataset:
Women’s participation in the offshore and inshore fisheries entrepreneurship

Purpose The purpose of this paper is to critically examine the multinational oil companies’ corporate social responsibility (CSR) initiatives in Nigeria. Its special focus is to investigate the impact of the global memorandum of understanding (GMoU) on women involved in offshore and inshore fisheries entrepreneurship in the coastal communities of the Niger Delta region. Design/methodology/approach This paper adopts a survey research technique, aimed at gathering information from a representative sample of the population, as it is essentially cross-sectional, describing and interpreting the current situation. A total 800 respondents were sampled across the coastal communities of the Niger Delta region. Findings The results from the use of a combined propensity score matching and logit model indicate that the GMoU model is gender insensitive, as extensive inequality restrains fisherwomen’s participation in the offshore and inshore fisheries entrepreneurship, often due to societal norms and customs that greatly frustrate women’s development in fisheries. Practical implications This implies that if fisherwomen continue in this unfavourable position, their reliance on menfolk would remain while trying to access financial support and decision-making regarding fisheries entrepreneurship development. Social implications The inshore and offshore fisheries entrepreneurship development can only succeed if cluster development boards of GMoUs are able to draw all the resources and talents and if fisherwomen are able to participate fully in the GMoUs intervention plans and programme. Originality/value This research contributes to the gender debate in fisheries entrepreneurship development from a CSR perspective in developing countries and rationale for demands for social projects by host communities. It concludes that business has an obligation to help in solving problems of public concern, and that CSR priorities in Sub-Saharan Africa should be aimed towards addressing the peculiarity of the socio-economic development challenges of the countries and be informed by socio-cultural influences.
Siamese Tracking Network With Informative Enhanced Loss

Designing an effective and uniform framework to meliorate tracking performance is very meaningful and essential. However, existing methods merely focus on single positive and negative instances corresponding to the exemplar, thoroughly ignoring the effective information hidden in other instances. To tackle this issue, in this paper, we present an informative enhanced loss based Siamese tracking network. Specifically, we introduce an informative enhanced loss to enable the network to capture information from an overall perspective. In other words, we construct dense connections among instances and exemplar. More importantly, we prove that our proposed loss can be transformed into the logistic loss and the triplet loss under particular parameter settings. Experiments on prevalent benchmarks demonstrate that the Siamese frameworks trained with our proposed loss indeed obtain better tracking results than original ones, and achieve promising performance against several state-of-the-art trackers on the real-time challenge.
Histopathological Image Analysis for Breast Cancer Detection Using Cubic SVM

Taking into consideration of world cancer report given by the World Health Organization (WHO) among women, breast cancer is the disease with the highest mortality rate worldwide. In Cancer disease, total of 25.2% of patients falls under the category of breast cancer. One of the main reasons behind the failure of saving cancer patients is due to latedetection and lacks of objective diagnosis in the type and level of cancer. Early detection of cancer has been improved due to evolutions in expert system and machine learning techniques with higher detection competence, for Computer-aided diagnosis. In this paper, histopathology-based feature has been taken into consideration for breast cancer detection and classification. The experimental analysis of the proposed approach has been done on publicly available dataset BreakHis. For experimental purpose we have tested K-Nearest neighbor (KNN), Random forest, and about six flavors of (SVM) Support Vector classification algorithms. The experimental result shows that proposed approach for detection and classification rate of breast cancer has been achieved maximum 92.3%accuracy with a cubic SVM classifier. The analysis of the results is verified with the help of classifier goodness parameters like accuracy, precision, recall, f-score, specificity, confusion matrix and ROC curve.
Target Inference in Argument Conclusion Generation

In argumentation, people state premises to reason towards a conclusion.The conclusion conveys a stance towards some target, such as a concept or statement.Often, the conclusion remains implicit, though, since it is self-evident in a discussion or left out for rhetorical reasons.However, the conclusion is key to understanding an argument, and hence, to any application that processes argumentation.We thus study the question to what extent an argument's conclusion can be reconstructed from its premises.In particular, we argue here that a decisive step is to infer a conclusion's target, and we hypothesize that this target is related to the premises' targets.We develop two complementary target inference approaches: one ranks premise targets and selects the top-ranked target as the conclusion target, the other finds a new conclusion target in a learned embedding space using a triplet neural network.Our evaluation on corpora from two domains indicates that a hybrid of both approaches is best, outperforming several strong baselines.According to human annotators, we infer a reasonably adequate conclusion target in 89% of the cases.
Direct estimation of quantum coherence by collective measurements

Abstract The recently established resource theory of quantum coherence allows for a quantitative understanding of the superposition principle, with applications reaching from quantum computing to quantum biology. While different quantifiers of coherence have been proposed in the literature, their efficient estimation in today’s experiments remains a challenge. Here, we introduce a collective measurement scheme for estimating the amount of coherence in quantum states, which requires entangled measurements on two copies of the state. As we show by numerical simulations, our scheme outperforms other estimation methods based on tomography or adaptive measurements, leading to a higher precision in a large parameter range for estimating established coherence quantifiers of qubit and qutrit states. We show that our method is accessible with today’s technology by implementing it experimentally with photons, finding a good agreement between experiment and theory.
#toolittletoolate: JUUL-related content on Instagram before and after self-regulatory action

Digital e-cigarette marketing is largely unregulated and remains easily accessible to young people. The growing public concern around youth JUUL use and its viral presence on social media led the company to engage in several voluntary actions to remove and reduce JUUL-related content on Instagram in May 2018. The current study examined how JUUL-related Instagram content changed in the US following JUUL Labs' wave of voluntary actions in May 2018.In 2019, we collected a total of 50,817 JUUL-relevant posts by 16,323 unique users on Instagram from March 1-May 15, 2018 (Phase 1) and May 16-November 11, 2018 (Phase II) using the application programming interface. We conducted a semantic network analysis to identify major topic clusters over time.Approximately 14,838 JUUL-related posts were made by 5,201 accounts in Phase I and 35,979 posts were made by 11,122 accounts in Phase II. Major content clusters remained unchanged over time-key topics were JUUL-related product characteristics and JUUL-communities; the general vape community; and cannabis-related behavior. Of note, cannabis-related content grew in Phase II, particularly use of the term CBD.Our results reflect the limits of voluntary industry actions to reduce or change vaping-related content on social media. Rather, strong federal restriction on commercial tobacco marketing is the optimal pathway to reduce initial product marketing exposure among youth. These limits would make the emergence and viral contagion of brand-related social media content less likely and reduce its influence on youth behavior.
A High-Level Comparison between the NIST Cyber Security Framework and the ISO 27001 Information Security Standard

This paper provides a high-level comparison between the National Institute of Standards and Technology's (NIST) Cyber Security Framework and the ISO 27001 Information Security Standard. Pros, cons and the advantages each framework holds over the other and how an organization would select an appropriate framework between CSF and ISO 27001 have been discussed along with a detailed comparison of how major security controls framework/guidelines like NIST SP 800-53, CIS Top-20 and ISO 27002 can be mapped back to each.
A fog based recommendation system for promoting the performance of E-Learning environments

Recently, Recommendation Systems (RSs) have gained a great interest. E-Learning is one of the most important working fields of RS in which many challenges that hinder users in discovering the most appropriate materials can be overcome. The fog computing technique can enrich E-Learning based RS as it bridges the gap between; the cloud and end devices. In this paper, we propose Fog based Recommendation System (FBRS), which can be successfully utilized for promoting the performance of the E-Learning environment. We discuss a framework to consolidate and improve EL environment through defining three modules of FBRS: (i) Class Identification Module (CIM), (ii) Subclass Identification Module (SIM), and (iii) Matchmaking Module (MM). Moreover, the FBRS approach achieves a high response time and security to overcome both personalization and synonymy. Experimental results show that FBRS outperforms are recent techniques in terms of recommendation accuracy.
TAdaNet: Task-Adaptive Network for Graph-Enriched Meta-Learning

Annotated data samples in real-world applications are often limited. Meta-learning, which utilizes prior knowledge learned from related tasks and generalizes to new tasks of limited supervised experience, is an effective approach for few-shot learning. However, standard meta-learning with globally shared knowledge cannot handle the task heterogeneity problem well, i.e., tasks lie in different distributions. Recent advances have explored several ways to trigger task-dependent initial parameters or metrics, in order to customize task-specific information. These approaches learn task contextual information from data, but ignore external domain knowledge that can help in the learning process. In this paper, we propose a task-adaptive network (TAdaNet) that makes use of a domain-knowledge graph to enrich data representations and provide task-specific customization. Specifically, we learn a task embedding that characterizes task relationships and tailors task-specific parameters, resulting in a task-adaptive metric space for classification. Experimental results on a few-shot image classification problem show the effectiveness of the proposed method. We also apply it on a real-world disease classification problem, and show promising results for clinical decision support.
An Improved Deep Fusion CNN for Image Recognition

With the development of Deep Convolutional Neural Networks (DCNNs), the extracted features for image recognition tasks have shifted from low-level features to the high-level semantic features of DCNNs. Previous studies have shown that the deeper the network is, the more abstract the features are. However, the recognition ability of deep features would be limited by insufficient training samples. To address this problem, this paper derives an improved Deep Fusion Convolutional Neural Network (DF-Net) which can make full use of the differences and complementarities during network learning and enhance feature expression under the condition of limited datasets. Specifically, DF-Net organizes two identical subnets to extract features from the input image in parallel, and then a well-designed fusion module is introduced to the deep layer of DF-Net to fuse the subnet’s features in multi-scale. Thus, the more complex mappings are created and the more abundant and accurate fusion features can be extracted to improve recognition accuracy. Furthermore, a corresponding training strategy is also proposed to speed up the convergence and reduce the computation overhead of network training. Finally, DF-Nets based on the well-known ResNet, DenseNet and MobileNetV2 are evaluated on CIFAR100, Stanford Dogs, and UECFOOD-100. Theoretical analysis and experimental results strongly demonstrate that DF-Net enhances the performance of DCNNs and increases the accuracy of image recognition.
Overview of CLEF HIPE 2020: Named Entity Recognition and Linking on Historical Newspapers

This paper presents an overview of the first edition of HIPE (Identifying Historical People, Places and other Entities), a pioneering shared task dedicated to the evaluation of named entity processing on historical newspapers in French, German and English. Since its introduction some twenty years ago, named entity (NE) processing has become an essential component of virtually any text mining application and has undergone major changes. Recently, two main trends characterise its developments: the adoption of deep learning architectures and the consideration of textual material originating from historical and cultural heritage collections. While the former opens up new opportunities, the latter introduces new challenges with heterogeneous, historical and noisy inputs. In this context, the objective of HIPE, run as part of the CLEF 2020 conference, is threefold: strengthening the robustness of existing approaches on non-standard inputs, enabling performance comparison of NE processing on historical texts, and, in the long run, fostering efficient semantic indexing of historical documents. Tasks, corpora, and results of 13 participating teams are presented.
A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine

The smart grid is vulnerable to network attacks, thus requiring a high detection rate and fast detection speed for intrusion detection systems. With a fast training speed and a strong model generalization ability, the extreme learning machine (ELM) perfectly meets the needs of intrusion detection of the smart grid. In this paper, the ELM is applied to the field of smart grid intrusion detection. Aiming at the problem that the randomness of input weights and hidden layer bias in the ELM cannot guarantee the optimal performance of the ELM intrusion detection model, a genetic algorithm (GA)-ELM algorithm based on a genetic algorithm (GA) is proposed. GA is used to optimize the input weight and hidden layer bias of the ELM. Firstly, the input weight and hidden layer bias of the ELM are mapped to the chromosome vector of a GA, and the test error of the ELM model is set as the fitness function of the GA. Then, the parameters of the ELM intrusion detection model are optimized by genetic operation; the input weight and bias, corresponding to the minimum test error, are selected to improve the performance of the ELM model. Compared with the ELM and online sequential extreme learning machine (OS-ELM), the GA-ELM effectively improves the accuracy, detection rate and precision of intrusion detection and reduces the false positive rate and missing report rate.
A Real-Time Vehicle Detection System under Various Bad Weather Conditions Based on a Deep Learning Model without Retraining

Numerous vehicle detection methods have been proposed to obtain trustworthy traffic data for the development of intelligent traffic systems. Most of these methods perform sufficiently well under common scenarios, such as sunny or cloudy days; however, the detection accuracy drastically decreases under various bad weather conditions, such as rainy days or days with glare, which normally happens during sunset. This study proposes a vehicle detection system with a visibility complementation module that improves detection accuracy under various bad weather conditions. Furthermore, the proposed system can be implemented without retraining the deep learning models for object detection under different weather conditions. The complementation of the visibility was obtained through the use of a dark channel prior and a convolutional encoder–decoder deep learning network with dual residual blocks to resolve different effects from different bad weather conditions. We validated our system on multiple surveillance videos by detecting vehicles with the You Only Look Once (YOLOv3) deep learning model and demonstrated that the computational time of our system could reach 30 fps on average; moreover, the accuracy increased not only by nearly 5% under low-contrast scene conditions but also 50% under rainy scene conditions. The results of our demonstrations indicate that our approach is able to detect vehicles under various bad weather conditions without the need to retrain a new model.
Software Defect Prediction Using Variant based Ensemble Learning and Feature Selection Techniques

Testing is considered as one of the expensive activities in software development process.Fixing the defects during testing process can increase the cost as well as the completion time of the project.Cost of testing process can be reduced by identifying the defective modules during the development (before testing) stage.This process is known as "Software Defect Prediction", which has been widely focused by many researchers in the last two decades.This research proposes a classification framework for the prediction of defective modules using variant based ensemble learning and feature selection techniques.Variant selection activity identifies the best optimized versions of classification techniques so that their ensemble can achieve high performance whereas feature selection is performed to get rid of such features which do not participate in classification and become the cause of lower performance.The proposed framework is implemented on four cleaned NASA datasets from MDP repository and evaluated by using three performance measures, including: F-measure, Accuracy, and MCC.According to results, the proposed framework outperformed 10 widely used supervised classification techniques, including: "Naïve Bayes (NB), Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF)".
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Crosslingual Lexical Semantic Similarity

We introduce Multi-SimLex, a large-scale lexical resource and evaluation benchmark covering data sets for 12 typologically diverse languages, including major languages (e.g., Mandarin Chinese, Spanish, Russian) as well as less-resourced ones (e.g., Welsh, Kiswahili). Each language data set is annotated for the lexical relation of semantic similarity and contains 1,888 semantically aligned concept pairs, providing a representative coverage of word classes (nouns, verbs, adjectives, adverbs), frequency ranks, similarity intervals, lexical fields, and concreteness levels. Additionally, owing to the alignment of concepts across languages, we provide a suite of 66 crosslingual semantic similarity data sets. Because of its extensive size and language coverage, Multi-SimLex provides entirely novel opportunities for experimental evaluation and analysis. On its monolingual and crosslingual benchmarks, we evaluate and analyze a wide array of recent state-of-the-art monolingual and crosslingual representation models, including static and contextualized word embeddings (such as fastText, monolingual and multilingual BERT, XLM), externally informed lexical representations, as well as fully unsupervised and (weakly) supervised crosslingual word embeddings. We also present a step-by-step data set creation protocol for creating consistent, Multi-Simlex–style resources for additional languages. We make these contributions—the public release of Multi-SimLex data sets, their creation protocol, strong baseline results, and in-depth analyses which can be helpful in guiding future developments in multilingual lexical semantics and representation learning—available via a Web site that will encourage community effort in further expansion of Multi-Simlex to many more languages. Such a large-scale semantic resource could inspire significant further advances in NLP across languages.
simstudy: Illuminating research methods through data generation

The simstudy package is a collection of functions for R (R Core Team, 2020) that allow users to generate simulated data sets in order to explore modeling techniques or better understand data generating processes.The user defines the distributions of individual variables, specifies relationships between covariates and outcomes, and generates data based on these specifications.The final data sets can represent randomized control trials, repeated measure designs, cluster-randomized trials, or naturally observed data processes.Many other complexities can be added, including survival data, correlated data, factorial study designs, step wedge designs, and missing data processes.
DocStruct: A Multimodal Method to Extract Hierarchy Structure in Document for General Form Understanding

Form understanding depends on both textual contents and organizational structure. Although modern OCR performs well, it is still challenging to realize general form understanding because forms are commonly used and of various formats. The table detection and handcrafted features in previous works cannot apply to all forms because of their requirements on formats. Therefore, we concentrate on the most elementary components, the key-value pairs, and adopt multimodal methods to extract features. We consider the form structure as a tree-like or graph-like hierarchy of text fragments. The parent-child relation corresponds to the key-value pairs in forms. We utilize the state-of-the-art models and design targeted extraction modules to extract multimodal features from semantic contents, layout information, and visual images. A hybrid fusion method of concatenation and feature shifting is designed to fuse the heterogeneous features and provide an informative joint representation. We adopt an asymmetric algorithm and negative sampling in our model as well. We validate our method on two benchmarks, MedForm and FUNSD, and extensive experiments demonstrate the effectiveness of our method.
Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning

Abductive and counterfactual reasoning, core abilities of everyday human cognition, require reasoning about what might have happened at time t, while conditioning on multiple contexts from the relative past and future.However, simultaneous incorporation of past and future contexts using generative language models (LMs) can be challenging, as they are trained either to condition only on the past context or to perform narrowly scoped text-infilling.In this paper, we propose DELOREAN, a new unsupervised decoding algorithm that can flexibly incorporate both the past and future contexts using only off-the-shelf, left-to-right language models and no supervision.The key intuition of our algorithm is incorporating the future through back-propagation, during which, we only update the internal representation of the output while fixing the model parameters.By alternating between forward and backward propagation, DELOREAN can decode the output representation that reflects both the left and right contexts.We demonstrate that our approach is general and applicable to two nonmonotonic reasoning tasks: abductive text generation and counterfactual story revision, where DELOREAN outperforms a range of unsupervised and some supervised methods, based on automatic and human evaluation. 1