nils-herrmann/clean_scirepeval huggingface dataset:
Reliability-based Design of Ship Structures : Current Practice and Emerging Technologies

The development of reliability-based design criteria for surface ship structures needs to consider the following three components: (1) loads, (2) structural strength, and (3) methods of reliability analysis. A methodology for reliability-based design of ship structures is provided in this document. The methodology consists of the following two approaches: (1) direct reliabilitybased design, and (2) load and resistance factor design (LRFD) rules. According to this methodology, loads can be linearly or nonlinearly treated. Also in assessing structural strength, linear or nonlinear analysis can be used. The reliability assessment and reliability-based design can be performed at several levels of a structural system, such as at the hull-girder, grillage, panel, plate and detail levels. A rational treatment of uncertainty is suggested by considering all its types. Also, failure definitions can have significant effects on the assessed reliability, or resulting reliability-based designs. A method for defining and classifying failures at the system level is provided. The method considers the continuous nature of redundancy in ship structures. A bibliography is provided at the end of this document to facilitate future implementation of the methodology.
Nature Inspired Metaheuristic Algorithms for Multilevel Thresholding Image Segmentation - A Survey

Segmentation is one of the essential tasks in image processing. Thresholding is one of the simplest techniques for performing image segmentation. Multilevel thresholding is a simple and effective technique. The primary objective of bi-level or multilevel thresholding for image segmentation is to determine a best thresholding value. To achieve multilevel thresholding various techniques has been proposed. A study of some nature inspired metaheuristic algorithms for multilevel thresholding for image segmentation is conducted. Here, we study about Particle swarm optimization (PSO) algorithm, artificial bee colony optimization (ABC), Ant colony optimization (ACO) algorithm and Cuckoo search (CS) algorithm. Ant colony optimization, Artificial bee colony optimization, Cuckoo search algorithm, Image segmentation, Multilevel thresholding, Particle swarm optimization.
Crowdsourcing the Verification of Fake News and Alternative Facts

Fake news and alternative facts have dominated the news cycle of late. In this paper, we present a prototype system that uses social argumentation to verify the validity of proposed alternative facts and help in the detection of fake news. We utilize fundamental argumentation ideas in a graph-theoretic framework that also incorporates semantic web and linked data principles. The argumentation structure is crowdsourced and mediated by expert moderators in a virtual community.
A review on mindfulness and nursing stress among nurses

Previous research has been done on mindfulness and nursing stress but no review has been done to highlight the most up-to-date findings, to justify the recommendation of mindfulness training for the nursing field. The present paper aims to review the relevant studies, derive conclusions, and discuss future direction of research in this field.A total of 19 research papers were reviewed. The majority was intervention studies on the effects of mindfulness-training programs on nursing stress. Higher mindfulness is correlated with lower nursing stress. Mindfulness-based training programs were found to have significant positive effects on nursing stress and psychological well-being. The studies were found to have non-standardized intervention methods, inadequate research designs, small sample size, and lack of systematic follow-up on the sustainability of treatment effects, limiting the generalizability of the results. There is also a lack of research investigation into the underlying mechanism of action of mindfulness on nursing stress. Future research that addresses these limitations is indicated.
Multi-lingual Common Semantic Space Construction via Cluster-Consistent Word Embedding

We construct a multilingual common semantic space based on distributional semantics, where words from multiple languages are projected into a shared space via which all available resources and knowledge can be shared across multiple languages. Beyond word alignment, we introduce multiple cluster-level alignments and enforce the word clusters to be consistently distributed across multiple languages. We exploit three signals for clustering: (1) neighbor words in the monolingual word embedding space; (2) character-level information; and (3) linguistic properties (e.g., apposition, locative suffix) derived from linguistic structure knowledge bases available for thousands of languages. We introduce a new cluster-consistent correlational neural network to construct the common semantic space by aligning words as well as clusters. Intrinsic evaluation on monolingual and multilingual QVEC tasks shows our approach achieves significantly higher correlation with linguistic features which are extracted from manually crafted lexical resources than state-of-the-art multi-lingual embedding learning methods do. Using low-resource language name tagging as a case study for extrinsic evaluation, our approach achieves up to 14.6% absolute F-score gain over the state of the art on cross-lingual direct transfer. Our approach is also shown to be robust even when the size of bilingual dictionary is
Benefits of using artificial intelligence in medical predictions

Medicine is a domain where predictions are very important. This paper encourages the use of artificial intelligence (AI) as technological support for decision-making in medical life. Not because the human intelligence would not be enough, but because the mechanisms of artificial intelligence have several benefits that are suitable for this area. These positive features come as a result of combining two strong qualities in the same systems: the precision of mathematics and the power of current technologies. It would be a real waste to ignore them.
Certain factor analysis for extra pulmonary tuberculosis diagnosis

Tuberculosis is an infectious disease that is easily transmitted through the air. Tuberculosis mostly attacks the lungs, but this disease can also attack other organs called extra-pulmonary. The Extra pulmonary tuberculosis can endanger the safety of community and slow in initial diagnosis because of the lack of knowledge. The purpose of this research is to build expert system of the extra pulmonary tuberculosis disease diagnosis. The system can facilitate the user in making the initial diagnosis of the disease, so patients can get the right handling. The system uses forward chaining inference and a Web-based Certainty Factor method with an accuracy rate of 85% has been utilized to assist health and community.
An overview of relations among causal modelling methods.

This paper provides a brief overview to four major types of causal models for health-sciences research: Graphical models (causal diagrams), potential-outcome (counterfactual) models, sufficient-component cause models, and structural-equations models. The paper focuses on the logical connections among the different types of models and on the different strengths of each approach. Graphical models can illustrate qualitative population assumptions and sources of bias not easily seen with other approaches; sufficient-component cause models can illustrate specific hypotheses about mechanisms of action; and potential-outcome and structural-equations models provide a basis for quantitative analysis of effects. The different approaches provide complementary perspectives, and can be employed together to improve causal interpretations of conventional statistical results.
A hybrid approach to the sentiment analysis problem at the sentence level

This doctoral thesis deals with a number of challenges related to investigating and devising solutions to the Sentiment Analysis Problem, a subset of the discipline known as Natural Language Processing (NLP), following a path that differs from the most common approaches currently in-use. The majority of the research and applications building in Sentiment Analysis (SA) / Opinion Mining (OM) have been conducted and developed using Supervised Machine Learning techniques. It is our intention to prove that a hybrid approach merging fuzzy sets, a solid sentiment lexicon, traditional NLP techniques and aggregation methods will have the effect of compounding the power of all the positive aspects of these tools. In this thesis we will prove three main aspects, namely: 1. That a Hybrid Classification Model based on the techniques mentioned in the previous paragraphs will be capable of: (a) performing same or better than established Supervised Machine Learning techniques -namely, Naı̈ve Bayes and Maximum Entropy (ME)when the latter are utilised respectively as the only classification methods being applied, when calculating subjectivity polarity, and (b) computing the intensity of the polarity previously estimated. 2. That cross-ratio uninorms can be used to effectively fuse the classification outputs of several algorithms producing a compensatory effect. 3. That the Induced Ordered Weighted Averaging (IOWA) operator is a very good choice to model the opinion of the majority (consensus) when the outputs of a number of classification methods are combined together. For academic and experimental purposes we have built the proposed methods and associated prototypes in an iterative fashion: • Step 1: we start with the so-called Hybrid Standard Classification (HSC) method, responsible for subjectivity polarity determination. • Step 2: then, we have continued with the Hybrid Advanced Classification (HAC) method that computes the polarity intensity of opinions/sentiments. • Step 3: in closing, we present two methods that produce a semantic-specific aggregation of two or more classification methods, as a complement to the HSC/HAC methods when the latter cannot generate a classification value or when we are looking for an aggregation that implies consensus, respectively: ◦ the Hybrid Advanced Classification with Aggregation by Cross-ratio Uninorm (HACACU) method. ◦ the Hybrid Advanced Classification with Aggregation by Consensus (HACACO) method.
A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, such methods cannot well capture the contextualized local information in the matching process. To tackle this problem, we present a new deep architecture to match two sentences with multiple positional sentence representations. Specifically, each positional sentence representation is a sentence representation at this position, generated by a bidirectional long short term memory (Bi-LSTM). The matching score is finally produced by aggregating interactions between these different positional sentence representations, through k-Max pooling and a multi-layer perceptron. Our model has several advantages: (1) By using Bi-LSTM, rich context of the whole sentence is leveraged to capture the contextualized local information in each positional sentence representation; (2) By matching with multiple positional sentence representations, it is flexible to aggregate different important contextualized local information in a sentence to support the matching; (3) Experiments on different tasks such as question answering and sentence completion demonstrate the superiority of our model.
Hybrid de-anonymization across real-world heterogeneous social networks

To enjoy various utility and services, people are active in multiple social networks nowadays. With tons of data generated on platforms, multiple accounts of the same user in different social networks can be used to de-anonymize the user in a large scale. The aggregation of user profiles poses a threat to user privacy. With a concern of privacy leakage, de-anonymization techniques, including graph based approaches and profile based approaches, are widely studied in recent years. However, few works throw light on the deanonymization between real-world heterogeneous social networks. In this paper, we propose a Hybrid De-anonymization Scheme (HDS) aiming at de-anonymizing heterogeneous social networks. HDS firstly leverages the network graph structure to significantly reduce the size of candidate set, then exploits user profile information to identify the correct mapping users with a high confidence. Performance evaluation on real-world social network datasets shows that HDS has considerable accuracy on de-anonymization and significantly outperforms the prior schemes.
Cloud-enabled remote health monitoring system

Sensor Web Enablement (SWE) for health care allows the access of sensor data anytime, anywhere using standard protocol and Application Program Interface (API). In this paper Open Geo-Spatial Consortium (OGC) standard based remote health monitoring system is proposed that allows integration of sensor and web using standard web based interface. The aim is to provide the data in an open & interoperable manner, and reduce data redundancy. Fixed specification is used for exchange of sensor data globally for all sensor networks. OGC SWEis applicable to different sensor systems including medical sensor networks. A standard format is used to document sensor descriptions and encapsulate data. Sensor data is ported on to cloud which provides scalability, centralized user access, persistent data storage and no infrastructure maintenance cost for heavy volumes of sensitive health data. Decision tree pruning algorithm with high confidence factor is proposed for automatic decision making.
Anticipating Traffic Accidents with Adaptive Loss and Large-Scale Incident DB

In this paper, we propose a novel approach for traffic accident anticipation through (i) Adaptive Loss for Early Anticipation (AdaLEA) and (ii) a large-scale self-annotated incident database for anticipation. The proposed AdaLEA allows a model to gradually learn an earlier anticipation as training progresses. The loss function adaptively assigns penalty weights depending on how early the model can anticipate a traffic accident at each epoch. Additionally, we construct a Near-miss Incident DataBase for anticipation. This database contains an enormous number of traffic near-miss incident videos and annotations for detail evaluation of two tasks, risk anticipation and risk-factor anticipation. In our experimental results, we found our proposal achieved the highest scores for risk anticipation (+6.6% better on mean average precision (mAP) and 2.36 sec earlier than previous work on the average time-to-collision (ATTC)) and risk-factor anticipation (+4.3% better on mAP and 0.70 sec earlier than previous work on ATTC).
Mycobacterium tuberculosis: success through dormancy.

Tuberculosis (TB) remains a major health threat, killing nearly 2 million individuals around this globe, annually. The only vaccine, developed almost a century ago, provides limited protection only during childhood. After decades without the introduction of new antibiotics, several candidates are currently undergoing clinical investigation. Curing TB requires prolonged combination of chemotherapy with several drugs. Moreover, monitoring the success of therapy is questionable owing to the lack of reliable biomarkers. To substantially improve the situation, a detailed understanding of the cross-talk between human host and the pathogen Mycobacterium tuberculosis (Mtb) is vital. Principally, the enormous success of Mtb is based on three capacities: first, reprogramming of macrophages after primary infection/phagocytosis to prevent its own destruction; second, initiating the formation of well-organized granulomas, comprising different immune cells to create a confined environment for the host-pathogen standoff; third, the capability to shut down its own central metabolism, terminate replication, and thereby transit into a stage of dormancy rendering itself extremely resistant to host defense and drug treatment. Here, we review the molecular mechanisms underlying these processes, draw conclusions in a working model of mycobacterial dormancy, and highlight gaps in our understanding to be addressed in future research.
Wasserstein Barycenter Model Ensembling

In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters. Optimal transport metrics, such as the Wasserstein distance, allow incorporating semantic side information such as word embeddings. Using W. barycenters to find the consensus between models allows us to balance confidence and semantics in finding the agreement between the models. We show applications of Wasserstein ensembling in attribute-based classification, multilabel learning and image captioning generation. These results show that the W. ensembling is a viable alternative to the basic geometric or arithmetic mean ensembling.
New Rogowski coil design with a high DV/DT immunity and high bandwidth

With the fast development of modern power semiconductors in the last years, the development of current measurement technologies has to adapt to this evolution. The challenge for the power electronic engineer is to provide a current sensor with a high bandwidth and a high immunity against external interferences. Rogowski current transducers are popular for monitoring transient currents in power electronic applications without interferences caused by external magnetic fields. But the trend of even higher current and voltage gradients generates a dilemma regarding the Rogowski current transducer technology. On the one hand, a high current gradient requires a current sensor with a high bandwidth. On the other hand, high voltage gradients forces to use a shielding around the Rogowski coil in order to protect the measurement signal from a capacitive displacement current caused by an unavoidable capacitive coupling to the setup, which reduces the bandwidth substantially. This paper presents a new Rogowski coil design which allows to measure high current gradients close to high voltage gradients without interferences and without reducing the bandwidth by a shielding. With this new measurement technique, it is possible to solve the mentioned dilemma and to get ready to measure the current of modern power semiconductors such as SiC and GaN with a Rogowski current transducer.
Analysis and optimization of a composite leaf spring

A four-leaf steel spring used in the rear suspension system of light vehicles is analyzed using ANSYS V5.4 software. The finite element results showing stresses and deflections verified the existing analytical and experimental solutions. Using the results of the steel leaf spring, a composite one made from fiberglass with epoxy resin is designed and optimized using ANSYS. Main consideration is given to the optimization of the spring geometry. The objective was to obtain a spring with minimum weight that is capable of carrying given static external forces without failure. The design constraints were stresses (Tsai–Wu failure criterion) and displacements. The results showed that an optimum spring width decreases hyperbolically and the thickness increases linearly from the spring eyes towards the axle seat. Compared to the steel spring, the optimized composite spring has stresses that are much lower, the natural frequency is higher and the spring weight without eye units is nearly 80% lower.
Relationship between English Language , Learning Strategies , Attitudes , Motivation , and Students ’ Academic Achievement

A considerable amount of research has been done to explore the key factors that affect learning a second language. Among these factors are students' learning strategies, motivation, attitude, learning environment, and the age at which they are exposed to a second language. Such issues have not been explored extensively in Saudi Arabia, even though English is used as the medium of teaching and learning for medical studies.  First, to explore the learning strategies used to study English as a second language. Second, to identify students' motivations for studying English. Third, to assess students' perceptions toward their learning environment. Fourth, to investigate students' attitude towards the speakers of English. Fifth, to explore any possible relationships among English language proficiency grades of students and the following: demographic variables, grades for their general medical courses, learning strategies, motivational variables, attitudes, and environmental variables. It is also the aim of this study to explore the relationships between English language learning strategies and motivational variables.  A cross-sectional descriptive study was conducted in May, 2008. The Attitudinal Measure of Learners of English as a Second Language (AMLESL) questionnaire was used to explore the learning strategies used by students to study English as a second language, their motivation to study English, their attitude toward English speaking people, and perceptions toward the environment where the learning is taking place.  A total of 110 out of 120 questionnaires were completed by Applied Medical Science undergraduates, yielding a response rate of 92%. Students utilize all types of learning strategies. Students were motivated 'integratively' and 'instrumentally'. There were significant correlations between the achievement in English and performance in general medical courses, learning strategies, motivation, age, and the formal level at which the student started to learn English.  The study showed that students utilize all types of language learning strategies. However, cognitive strategies were the most frequently utilized. Students considered their learning environment as more positive than negative. Students were happy with their teacher, and with their English courses. Students held a positive attitude toward English speaking people. Achievement in English was associated positively with performance in the general medical courses, motivation, and social learning strategies. Relationship between English Language, Learning Strategies, Attitudes, Motivation, and Students’ Academic Achievement
PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network

Partial differential equations (PDEs) are commonly derived based on empirical observations. However, recent advances of technology enable us to collect and store massive amount of data, which offers new opportunities for data-driven discovery of PDEs. In this paper, we propose a new deep neural network, called PDE-Net 2.0, to discover (time-dependent) PDEs from observed dynamic data with minor prior knowledge on the underlying mechanism that drives the dynamics. The design of PDE-Net 2.0 is based on our earlier work  where the original version of PDE-Net was proposed. PDE-Net 2.0 is a combination of numerical approximation of differential operators by convolutions and a symbolic multi-layer neural network for model recovery. Comparing with existing approaches, PDE-Net 2.0 has the most flexibility and expressive power by learning both differential operators and the nonlinear response function of the underlying PDE model. Numerical experiments show that the PDE-Net 2.0 has the potential to uncover the hidden PDE of the observed dynamics, and predict the dynamical behavior for a relatively long time, even in a noisy environment.