nils-herrmann/clean_scirepeval huggingface dataset:
A Unified Framework for Generalizable Style Transfer: Style and Content Separation

Image style transfer has drawn broad attention in recent years. However, most existing methods aim to explicitly model the transformation between different styles, and the learned model is thus not generalizable to new styles. We here propose a unified style transfer framework for both character typeface transfer and neural style transfer tasks leveraging style and content separation. A key merit of such framework is its generalizability to new styles and contents. The overall framework consists of style encoder, content encoder, mixer and decoder. The style encoder and content encoder are used to extract the style and content representations from the corresponding reference images. The mixer integrates the above two representations and feeds it into the decoder to generate images with the target style and content. During training, the encoder networks learn to extract styles and contents from limited size of style/content reference images. This learning framework allows simultaneous style transfer among multiple styles and can be deemed as a special ‘multi-task’ learning scenario. The encoders are expected to capture the underlying features for different styles and contents which is generalizable to new styles and contents. Under this framework, we design two individual networks for character typeface transfer and neural style transfer, respectively. For character typeface transfer, to separate the style features and content features, we leverage the conditional dependence of styles and contents given an image. For neural style transfer, we leverage the statistical information of feature maps in certain layers to represent style. Extensive experimental results have demonstrated the effectiveness and robustness of the proposed methods.
Joint Link-Attribute User Identity Resolution in Online Social Networks

In the modern Web, it is common for an active person to have several profiles in different online social networks. As new general-purpose and niche social network services arise every year, the problem of social data integration will likely remain actual in the nearest future. Discovering multiple profiles of a single person across different social networks allows to merge all user’s contacts from different social services or compose more complete social graph that is helpful in many social-powered applications. In this paper we propose a new approach for user profile matching based on Conditional Random Fields that extensively combines usage of profile attributes and social linkage. It is extremely suitable for cases when profile data is poor, incomplete or hidden due to privacy settings. Evaluation on Twitter and Facebook sample datasets showed that our solution significantly outperforms common attribute-based approach and is able to find matches that are not discoverable by using only profile information. We also demonstrate the importance of social links for identity resolution task and show that certain profiles can be matched based only on social relationships between online social networks users.
Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data

Over the past decade, machine learning techniques and in particular predictive modeling and pattern recognition in biomedical sciences, from drug delivery systems to medical imaging, have become one of the most important methods of assisting researchers in gaining a deeper understanding of issues in their entirety and solving complex medical problems. Deep learning is a powerful machine learning algorithm in classification that extracts low-to high-level features. In this paper, we employ a convolutional neural network to distinguish an Alzheimers brain from a normal, healthy brain. The importance of classifying this type of medical data lies in its potential to develop a predictive model or system in order to recognize the symptoms of Alzheimers disease when compared with normal subjects and to estimate the stages of the disease. Classification of clinical data for medical conditions such as Alzheimers disease has always been challenging, and the most problematic aspect has always been selecting the strongest discriminative features. Using the Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified functional MRI data of Alzheimers subjects from normal controls, where the accuracy of testing data reached 96.85%. This experiment suggests that the shift and scale invariant features extracted by CNN followed by deep learning classification represents the most powerful method of distinguishing clinical data from healthy data in fMRI. This approach also allows for expansion of the methodology to predict more complicated systems.
Labor market risks of industry 4.0, digitization, robots and AI

Digitization changes our world. Industry 4.0, the digital transformation of manufacturing changes the labor market. The impacts of rapid technology development of the fourth industrial revolution present huge challenges for the society and for policy makers. Are we facing reduction of employment by automation rendering human work force uncompetitive with machines? Can creation of new fields of employment, new types of jobs compensate for the loss of traditional labor market requirements?
"PageRank" for Argument Relevance

Future search engines are expected to deliver pro and con arguments in response to queries on controversial topics. While argument mining is now in the focus of research, the question of how to retrieve the relevant arguments remains open. This paper proposes a radical model to assess relevance objectively at web scale: the relevance of an argument’s conclusion is decided by what other arguments reuse it as a premise. We build an argument graph for this model that we analyze with a recursive weighting scheme, adapting key ideas of PageRank. In experiments on a large ground-truth argument graph, the resulting relevance scores correlate with human average judgments. We outline what natural language challenges must be faced at web scale in order to stepwise bring argument relevance to web search engines.
Fake News Detection

Fake news, one of the biggest new-age problems has the potential to mould opinions and influence decisions. The proliferation of fake news on social media and Internet is deceiving people to an extent which needs to be stopped. The existing systems are inefficient in giving a precise statistical rating for any given news claim. Also, the restrictions on input and category of news make it less varied. This paper proposes a system that classifies unreliable news into different categories after computing an F-score. This system aims to use various NLP and classification techniques to help achieve maximum accuracy.
The 13 th Annual General Assembly of the JAMU Expanding Frontiers-Challenges and Opportunities in Maritime Education and Training Three-Dimensional Engine Simulators with Unity 3 D Game Software

The difference between a computer game and a simulator can be a small one both require the same capabilities from the computer: realistic graphics, behavior consistent with the laws of physics, a variety of scenarios where difficulties can emerge, and some assessment technique to inform users of performance. Computer games are a multi-billion dollar industry in the United States, and as the production costs and complexity of games have increased, so has the effort to make their creation easier. Commercial software products have been developed to greatly simpl ify the game-making process, allowing developers to focus on content rather than on programming. This paper investigates Unity3D game creation software for making threedimensional engine-room simulators. Unity3D is arguably the best software product for game creation, and has been used for numerous popular and successful commercial games. Maritime universities could greatly benefit from making custom simulators to fit specific applications and requirements, as well as from reducing the cost of purchasing simulators. We use Unity3D to make a three-dimensional steam turbine simulator that achieves a high degree of realism. The user can walk around the turbine, open and close valves, activate pumps, and run the turbine. Turbine operating parameters such as RPM, condenser vacuum, lube oil temperature. and governor status are monitored. In addition, the program keeps a log of any errors made by the operator. We find that with the use of Unity3D, students and faculty are able to make custom three-dimensional ship and engine room simulators that can be used as training and evaluation tools.
Classification of sentence level sentiment analysis using cloud machine learning techniques

Cloud machine learning (CML) techniques offer contemporary machine learning services, with pre-trained models and a service to generate own personalized models. This paper presents a completely unique emotional modeling methodology for incorporating human feeling into intelligent systems. The projected approach includes a technique to elicit emotion factors from users, a replacement illustration of emotions and a framework for predicting and pursuit user’s emotional mechanical phenomenon over time. The neural network based CML service has better training concert and enlarged exactness compare to other large scale deep learning systems. Opinions are important to almost all human activities and cloud based sentiment analysis is concerned with the automatic extraction of sentiment related information from text. With the rising popularity and availability of opinion rich resources such as personal blogs and online appraisal sites, new opportunities and issues arise as people now, actively use information technologies to explore and capture others opinions. In the existing system, a segmentation ranking model is designed to score the usefulness of a segmentation candidate for sentiment classification. A classification model is used for predicting the sentiment polarity of segmentation. The joint framework is trained directly using the sentences annotated with only sentiment polarity, without the use of any syntactic or sentiment annotations in segmentation level. However the existing system still has issue with classification accuracy results. To improve the classification performance, in the proposed system, cloud integrate the support vector machine, naive bayes and neural network algorithms along with joint segmentation approaches has been proposed to classify the very positive, positive, neutral, negative and very negative features more effectively using important feature selection. Also to handle the outliers we apply modified k-means clustering method on the given dataset. It is used to cloud cluster the outliers and hence the label as well as unlabeled features is handled efficiently. From the experimental result, we conclude that the proposed system yields better performance than the existing system.
Post-stroke depression: relationships to functional impairment, coping strategies, and rehabilitation outcome.

This study examined the phenomenon of post-stroke depression and evaluated its impact on rehabilitation outcome. Sixty-four patients presenting to a rehabilitation program within weeks of first stroke were evaluated for depression through self-report measures and staff ratings. Patients also rated the particular coping strategies which they used in dealing with their illness and hospital stay. Physical and occupational therapists provided measures of functional impairment at admission and discharge. A high (47%) prevalence of depression was found in this population, with no overall differences observed between patients with right or left hemisphere lesions. Depressed patients, in comparison to non-depressed, evidenced greater functional impairment at both admission and discharge. However, both groups showed similar gains over the course of rehabilitation. Coping strategies employed by depressed patients appeared to reflect a lower level of participation in the rehabilitation process. A subgroup of patients evaluated 6 weeks after discharge revealed that depression was associated with a worsening on one measure of functional status. These findings indicate that depression is a frequent companion of stroke, that it is associated with degree of functional impairment, and that it may exert a negative impact on the rehabilitation process and outcome.
Composite Task-Completion Dialogue System via Hierarchical Deep Reinforcement Learning

Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks. For example, the agent needs to reserve a hotel and book a flight so that there leaves enough time for commute between arrival and hotel check-in. This paper addresses this challenge by formulating the task in the mathematical framework of options over Markov Decision Processes (MDPs), and proposing a hierarchical deep reinforcement learning approach to learning a dialogue manager that operates at different temporal scales. The dialogue manager consists of (1) a top-level dialogue policy that selects among subtasks or options, (2) a low-level dialogue policy that selects primitive actions to complete the subtask given by the top-level policy, and (3) a global state tracker that helps ensure all cross-subtask constraints be satisfied. Experiments on a travel planning task with simulated and real users show that our approach leads to significant improvements over two baselines, one based on handcrafted rules and the other based on flat deep reinforcement learning.
Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial

Type 2 diabetes is associated with a substantially increased risk of cardiovascular disease, but the role of lipid-lowering therapy with statins for the primary prevention of cardiovascular disease in diabetes is inadequately defined. We aimed to assess the effectiveness of atorvastatin 10 mg daily for primary prevention of major cardiovascular events in patients with type 2 diabetes without high concentrations of LDL-cholesterol.



2838 patients aged 40-75 years in 132 centres in the UK and Ireland were randomised to placebo (n=1410) or atorvastatin 10 mg daily (n=1428). Study entrants had no documented previous history of cardiovascular disease, an LDL-cholesterol concentration of 4.14 mmol/L or lower, a fasting triglyceride amount of 6.78 mmol/L or less, and at least one of the following: retinopathy, albuminuria, current smoking, or hypertension. The primary endpoint was time to first occurrence of the following: acute coronary heart disease events, coronary revascularisation, or stroke. Analysis was by intention to treat.



The trial was terminated 2 years earlier than expected because the prespecified early stopping rule for efficacy had been met. Median duration of follow-up was 3.9 years (IQR 3.0-4.7). 127 patients allocated placebo (2.46 per 100 person-years at risk) and 83 allocated atorvastatin (1.54 per 100 person-years at risk) had at least one major cardiovascular event (rate reduction 37% [95% CI -52 to -17], p=0.001). Treatment would be expected to prevent at least 37 major vascular events per 1000 such people treated for 4 years. Assessed separately, acute coronary heart disease events were reduced by 36% (-55 to -9), coronary revascularisations by 31% (-59 to 16), and rate of stroke by 48% (-69 to -11). Atorvastatin reduced the death rate by 27% (-48 to 1, p=0.059). No excess of adverse events was noted in the atorvastatin group.



Atorvastatin 10 mg daily is safe and efficacious in reducing the risk of first cardiovascular disease events, including stroke, in patients with type 2 diabetes without high LDL-cholesterol. No justification is available for having a particular threshold level of LDL-cholesterol as the sole arbiter of which patients with type 2 diabetes should receive statins. The debate about whether all people with this disorder warrant statin treatment should now focus on whether any patients are at sufficiently low risk for this treatment to be withheld.
El Sistema De Producción Y Operaciones

La importancia creciente de la Administración de la Producción/Operaciones está relacionada con la superación del enfoque racionalizador y centralizador de la misión de esta área de las organizaciones. El análisis, el diagnóstico y la visión estratégica de la Dirección de Operaciones permiten a la empresa acomodarse a los cambios que exige la economía moderna. Una efectiva gestión, con un flujo constante de la información, una organización del trabajo adecuada y una estructura que fomente la participación, son instrumentos imprescindibles para que las Operaciones haga su trabajo.
Environmental Applications of Semiconductor Photocatalysis

0009-2665/95/0795-0069$15.50/0 0 a tremendous set of environmental problems related to the remediation of hazardous wastes, contaminated groundwaters, and the control of toxic air contaminants. For example, the slow pace of hazardous waste remediation at military installations around the world is causing a serious delay in conversion of many of these facilities to civilian uses. Over the last 10 years problems related to hazardous waste remediation' have emerged as a high national and international priority. Problems with hazardous wastes at military installations are related in part to the disposal of chemical wastes in lagoons, underground storage tanks, and dump sites. As a consequence of these disposal practices, the surrounding soil and underlying groundwater aquifers have become contaminated with a variety of hazardous (i.e., toxic) chemicals. Typical wastes of concern include heavy metals, aviation fuel, military-vehicle fuel, solvents and degreasing agents, and chemical byproducts from weapons manufacturing. The projected costs for cleanup at more than 1800 military installations in the United States have been put at $30 billion; the time required for cleanup has been estimated to be more than 10 years. In the civilian sector, the elimination of toxic and hazardous chemical substances such as the halogenated hydrocarbons from waste effluents and previously contaminated sites has become a major concern. More than 540 million metric tons of hazardous solid and liquid waste are generated annually by more than 14000 installations in the United States. A significant fraction of these wastes are disposed on the land each year. Some of these wastes eventually contaminate groundwater and surface water. Groundwater contamination is likely to be the primary source of human contact with toxic chemicals emanating from more than 70% of the hazardous waste sites in the United States. General classes of compounds of concern include: solvents, volatile organics, chlorinated volatile organics, dioxins, dibenzofurans, pesticides, PCB's, chlorophenols, asbestos, heavy metals, and arsenic compounds. Some specific compounds of interest are 4-chlorophenol, pentachlorophenol, trichloroethylene (TCE), perchloroethylene (PCE), CCL, HCC4, CHZC12, ethylene dibromide, vinyl chloride, ethylene dichloride, methyl chloroform, p-chlorobenzene, and hexachlorocyclopentadiene. The occurrence of TCE, PCE, CFC-113 (i.e., Freon-113) and other grease-cutting agents in soils and groundwaters is widespread. In order to address this significant problem, extensive research is underway to develop advanced analytical, biochemical, and physicochemical methods for the characterization and elimination of hazardous chemical compounds from air, soil, and water. Advanced physicochemical processes such as semicon-
Trait emotional intelligence and the dark triad traits of personality.

This study presents the first behavioral genetic investigation of the relationships between trait emotional intelligence (trait EI or trait emotional self-efficacy) and the Dark Triad traits of narcissism, Machiavellianism, and psychopathy. In line with trait EI theory, the construct correlated positively with narcissism, but negatively with the other two traits. Generally, the correlations were consistent across the 4 factors and 15 facets of the construct. Cholesky decomposition analysis revealed that the phenotypic associations were primarily due to correlated genetic factors and secondarily due to correlated nonshared environmental factors, with shared environmental factors being nonsignificant in all cases. Results are discussed from the perspective of trait EI theory with particular reference to the issue of adaptive value.
Collaborative Denoising Auto-Encoders for Top-N Recommender Systems

Most real-world recommender services measure their performance based on the top-N results shown to the end users. Thus, advances in top-N recommendation have far-ranging consequences in practical applications. In this paper, we present a novel method, called Collaborative Denoising Auto-Encoder (CDAE), for top-N recommendation that utilizes the idea of Denoising Auto-Encoders. We demonstrate that the proposed model is a generalization of several well-known collaborative filtering models but with more flexible components. Thorough experiments are conducted to understand the performance of CDAE under various component settings. Furthermore, experimental results on several public datasets demonstrate that CDAE consistently outperforms state-of-the-art top-N recommendation methods on a variety of common evaluation metrics.
Strategies for mHealth Research: Lessons from 3 Mobile Intervention Studies

The capacity of Mobile Health (mHealth) technologies to propel healthcare forward is directly linked to the quality of mobile interventions developed through careful mHealth research. mHealth research entails several unique characteristics, including collaboration with technologists at all phases of a project, reliance on regional telecommunication infrastructure and commercial mobile service providers, and deployment and evaluation of interventions “in the wild”, with participants using mobile tools in uncontrolled environments. In the current paper, we summarize the lessons our multi-institutional/multi-disciplinary team has learned conducting a range of mHealth projects using mobile phones with diverse clinical populations. First, we describe three ongoing projects that we draw from to illustrate throughout the paper. We then provide an example for multidisciplinary teamwork and conceptual mHealth intervention development that we found to be particularly useful. Finally, we discuss mHealth research challenges (i.e. evolving technology, mobile phone selection, user characteristics, the deployment environment, and mHealth system “bugs and glitches”), and provide recommendations for identifying and resolving barriers, or preventing their occurrence altogether.
Synthetic and Natural Noise Both Break Neural Machine Translation

Character-based neural machine translation (NMT) models alleviate out-ofvocabulary issues, learn morphology, and move us closer to completely end-toend translation systems. Unfortunately, they are also very brittle and easily falter when presented with noisy data. In this paper, we confront NMT models with synthetic and natural sources of noise. We find that state-of-the-art models fail to translate even moderately noisy texts that humans have no trouble comprehending. We explore two approaches to increase model robustness: structure-invariant word representations and robust training on noisy texts. We find that a model based on a character convolutional neural network is able to simultaneously learn representations robust to multiple kinds of noise.