LLukas22/scidocs huggingface dataset:
Robust Named Entity Extraction from Large Spoken Archives

Traditional approaches to Information Extraction (IE) from speech input simply consist in applying text based methods to the output of an Automatic Speech Recognition (ASR) system. If it gives satisfaction with low Word Error Rate (WER) transcripts, we believe that a tighter integration of the IE and ASR modules can increase the IE performance in more difficult conditions. More specifically this paper focuses on the robust extraction of Named Entities from speech input where a temporal mismatch between training and test corpora occurs. We describe a Named Entity Recognition (NER) system, developed within the French Rich Broadcast News Transcription program ESTER, which is specifically optimized to process ASR transcripts and can be integrated into the search process of the ASR modules. Finally we show how some metadata information can be collected in order to adapt NER and ASR models to new conditions and how they can be used in a task of Named Entity indexation of spoken archives.
RFID based Logistic Management System using Internet of Things (IoT)

In the era of IoT and data analytics this paper has proposed system architecture for RFID based shipment tracking using Raspberry Pi equipped with RFID RC522 module. Light weight MQTT protocol is used for publishing real-time passive RFID tag data to CloudMQTT broker from Raspberry Pi. Further the logistics management and monitoring can be done using web based multiple platform GUI using MQTT subscribe message from MQTT broker. Using data analytics tool various logistic related data can be automated for better logistic management. Raspberry Pi uses python based Mosquitto MQTT client program to publish data to CloudMQTT broker using an IEEE 802.11 Wireless LAN NIC.
Soil and water conservation: Productivity and environmental protection. 3. edition

This book is a revision of a landmark book offering a broad coverage of the field of soil and water conservation while fully exploring the hazards posed by erosion, sedimentation, and pollution. Scope includes agriculture, engineering, mining, and other uses of land along with the techniques needed to conserve soil while maintaining environmental quality.
On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models

Weather information is an important factor in load forecasting models. Typically, load forecasting models are constructed and tested using actual weather readings. However, online operation of load forecasting models requires the use of weather forecasts, with associated weather forecast errors. These weather forecast errors inevitably lead to a degradation in model performance. This is an important factor in load forecasting but has not been widely examined in the literature. The main aim of this paper is to present a novel technique for minimizing the consequences of this degradation. In addition, a supplementary technique is proposed to model weather forecast errors to reflect current accuracy. The proposed technique utilizes a combination of forecasts from several load forecasting models (sub-models). The parameter estimation may thus be split into two parts: sub-model and combination parameter estimation. It is shown that the lowest PMSE corresponds to training the sub-models with actual weather but training the combiner with forecast weather.
Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination

The proliferation of cyber-physical systems introduces the fourth stage of industrialization, commonly known as Industry 4.0. The vertical integration of various components inside a factory to implement a flexible and reconfigurable manufacturing system, i.e., smart factory, is one of the key features of Industry 4.0. In this paper, we present a smart factory framework that incorporates industrial network, cloud, and supervisory control terminals with smart shop-floor objects such as machines, conveyers, and products. Then, we provide a classification of the smart objects into various types of agents and define a coordinator in the cloud. The autonomous decision and distributed cooperation between agents lead to high flexibility. Moreover, this kind of self-organized system leverages the feedback and coordination by the central coordinator in order to achieve high efficiency. Thus, the smart factory is characterized by a self-organized multi-agent system assisted with big data based feedback and coordination. Based on this model, we propose an intelligent negotiation mechanism for agents to cooperate with each other. Furthermore, the study illustrates that complementary strategies can be designed to prevent deadlocks by improving the agents’ decision making and the coordinator’s behavior. The simulation results assess the effectiveness of the proposed negotiation mechanism and deadlock prevention strategies. © 2016 Elsevier B.V. All rights reserved.
Qualification of PET scanners for use in multicenter cancer clinical trials: the American College of Radiology Imaging Network experience.

UNLABELLED
The PET Core Laboratory of the American College of Radiology Imaging Network (ACRIN) qualifies sites to participate in multicenter research trials by quantitatively reviewing submitted PET scans of uniform cylinders to verify the accuracy of scanner standardized uptake value (SUV) calibration and qualitatively reviewing clinical PET images from each site. To date, cylinder and patient data from 169 PET scanners have been reviewed, and 146 have been qualified.


METHODS
Each site is required to submit data from 1 uniform cylinder and 2 patient test cases. Submitted phantom data are analyzed by drawing a circular region of interest that encompasses approximately 90% of the diameter of the interior of the phantom and then recording the mean SUV and SD of each transverse slice. In addition, average SUVs are measured in the liver of submitted patient scans. These data illustrate variations of SUVs across PET scanners and across institutions, and comparison of results with values submitted by the site indicate the level of experience of PET camera operators in calculating SUVs.


RESULTS
Of 101 scanner applications for which detailed records of the qualification process were available, 12 (12%) failed because of incorrect SUV or normalization calibrations. For sites to pass, the average cylinder SUV is required to be 1.0 +/- 0.1. The average SUVs for uniform cylinder images for the most common scanners evaluated-Siemens Biograph PET/CT (n = 43), GE Discovery LS PET/CT (n = 15), GE Discovery ST PET/CT (n = 34), Philips Allegro PET (n = 5), and Philips Gemini PET/CT (n = 11)-were 0.99, 1.01, 1.00, 0.98, and 0.95, respectively, and the average liver SUVs for submitted test cases were 2.34, 2.13, 2.27, 1.73, and 1.92, respectively.


CONCLUSION
Minimizing errors in SUV measurement is critical to achieving accurate quantification in clinical trials. The experience of the ACRIN PET Core Laboratory shows that many sites are unable to maintain accurate SUV calibrations without additional training or supervision. This raises concerns about using SUVs to quantify patient data without verification.
Automatic Online Fake News Detection Combining Content and Social Signals

The proliferation and rapid diffusion of fake news on the Internet highlight the need of automatic hoax detection systems. In the context of social networks, machine learning (ML) methods can be used for this purpose. Fake news detection strategies are traditionally either based on content analysis (i.e. analyzing the content of the news) or - more recently - on social context models, such as mapping the news' diffusion pattern. In this paper, we first propose a novel ML fake news detection method which, by combining news content and social context features, outperforms existing methods in the literature, increasing their already high accuracy by up to 4.8%. Second, we implement our method within a Facebook Messenger chatbot and validate it with a real-world application, obtaining a fake news detection accuracy of 81.7%.
Randomized clinical trial of intravenous soybean oil alone versus soybean oil plus fish oil emulsion after gastrointestinal cancer surgery.

BACKGROUND
Specific immunonutrients may reduce the incidence of postoperative complications and shorten recovery time. This randomized trial evaluated the clinical efficacy of a fish oil emulsion on outcome and immune function after gastrointestinal cancer surgery.


METHODS
A total of 206 patients with gastrointestinal or colonic cancer were randomized to receive isocaloric and isonitrogenous intravenous infusions of either soybean oil alone (1.2 g per kg bodyweight per day; control group, 103 analysed) or soybean plus fish oil emulsion (1.0 and 0.2 g per kg per day respectively; treatment group, 100 analysed) over 20-24 h daily for 7 days after surgery.


RESULTS
Baseline data were comparable in the two groups. There were fewer infectious complications (four versus 12 on day 8; P = 0.066), systemic inflammatory response syndrome (SIRS) was significantly less common (four versus 13; P = 0.039) and hospital stay was significantly shorter (mean(s.d.) 15(5) versus 17(8) days; P = 0.041) in the treatment group. Total postoperative medical costs were comparable in the two groups (mean(s.d.) US $ 1269(254) and 1302(324) in treatment and control groups respectively; P = 0.424). The median (interquartile range) difference in CD4/CD8 between days 1 and 8 after surgery was + 0.30 (0.06 to 0.79) in patients receiving fish oil and + 0.20 (-0.19 to 0.55) in controls (P = 0.021). No severe adverse events occurred in either group.


CONCLUSION
Fish oil emulsion-supplemented parenteral nutrition significantly reduced SIRS and length of hospital stay. These clinical benefits may be related to normalization of cellular immune functions and modulation of the inflammatory response.
Phase Mapper: Accelerating Materials Discovery with AI

uted to the discovery and characterization of new materials. The discovery of semiconductors laid the foundation for modern electronics, while the formulation of new molecules allows us to treat diseases previously thought incurable. Looking into the future, some of the largest problems facing humanity now are likely to be solved by the discovery of new materials. In this article, we explore the techniques materials scientists are using and show how our novel artificial intelligence system, Phase-Mapper, allows materials scientists to quickly solve material systems to infer their underlying crystal structures and has led to the discovery of new solar light absorbers. Articles
Surface Normals in the Wild

We study the problem of single-image depth estimation for images in the wild. We collect human annotated surface normals and use them to help train a neural network that directly predicts pixel-wise depth. We propose two novel loss functions for training with surface normal annotations. Experiments on NYU Depth, KITTI, and our own dataset demonstrate that our approach can significantly improve the quality of depth estimation in the wild.
Business Process Management: A Survey among Small and Medium Sized Enterprises

The purpose of this research is to evaluate to which degree small and medium-sized enterprises (SMEs) use process management techniques and methods. The findings result from 20 interviews that were conducted in 11 smalland medium-sized enterprises in the manufacturing and service sector in Austria. The information retrieved from the interviews was used to rate the overall process management performance of the company. The underlying maturity model for this rating process is based on an adaption of Michael Hammers Process and Enterprise Maturity Model (PEMM). Service companies, in average, reached a higher maturity level than manufacturer did. Also companies that are active in the IT sector had a higher maturity level than companies from other industries. It turned out that there seems to be a strong connection between leadership and the maturity of business process management within the company. This article provides an insight into the process-related activities of small and medium enterprises in Austria. It is explained how business process management is relevant to SMEs.
TUTA1 at the NTCIR-11 Temporalia Task

This paper details our participation in the NTCIR-11 Temporalia task including Temporal Query Intent Classification (TQIC) and Temporal Information Retrieval (TIR). In the TQIC subtask, we explore the rich temporal information in the labeled and unlabeled search queries. Semi-supervised and supervised linear classifiers are learned to predict the temporal classes for each search query. In the TIR subtask, we perform temporal ranking based on the technique of learning-to-rank. Two classes of features are investigated for estimating the document relevance.
E-government and its Situation in E-city

The World Wide Web and Internet have been two of the most important developments of information technology which has provided government with capability to increase electronic services. In this paper, at first we will investigate definition of e-government, steps of e-government evolution, its challenges and its situation in our country (Iran). And finally E-city and e-citizen as a vital element in e-government will be discussed.
Mixed-Transfer: Transfer Learning over Mixed Graphs

Heterogeneous transfer learning has been proposed as a new learning strategy to improve performance in a target domain by leveraging data from other heterogeneous source domains where feature spaces can be different across different domains. In order to connect two different spaces, one common technique is to bridge feature spaces by using some co-occurrence data. For example, annotated images can be used to build feature mapping from words to image features, and then applied on text-to-image knowledge transfer. However, in practice, such co-occurrence data are often from Web, e.g. Flickr, and generated by users. That means these data can be sparse and contain personal biases. Directly building models based on them may fail to provide reliable bridge. To solve these aforementioned problems, in this paper, we propose a novel algorithm named MixedTransfer. It is composed of three components, that is, a cross domain harmonic function to avoid personal biases, a joint transition probability graph of mixed instances and features to model the heterogeneous transfer learning problem, a random walk process to simulate the label propagation on the graph and avoid the data sparsity problem. We conduct experiments on 171 real-world tasks, showing that the proposed approach outperforms four state-of-the-art heterogeneous transfer learning algorithms.
Motivating factors influencing college students' choice of academic major.

OBJECTIVES
To assess the factors, motivations, and nonacademic influences that affected the choice of major among pharmacy and nonpharmacy undergraduate students.


METHODS
A survey was administered to 618 pharmacy and nonpharmacy majors to assess background and motivational factors that may have influenced their choice of major. The sample consisted of freshman and sophomore students enrolled in a required speech course.


RESULTS
African-American and Hispanic students were less likely to choose pharmacy as a major than Caucasians, whereas Asian-Americans were more likely to choose pharmacy as a major. Pharmacy students were more likely to be interested in science and math than nonpharmacy students.


CONCLUSION
Students' self-reported racial/ethnic backgrounds influence their decision of whether to choose pharmacy as their academic major. Results of this survey provide further insight into developing effective recruiting strategies and enhancing the marketing efforts of academic institutions.
Daily life activities as mediators of the relationship between personality variables and subjective well-being among older adults

The purpose of the present study was to examine the mediational role of participation in daily life activities on the relationship between personality variables, such as self-esteem and optimism, and subjective well-being in Spanish older adults. Two hundred and fifty people (150 retirees and 100 workers) from late adulthood to old age were interviewed to complete the Rosenberg Scale, the Optimism Scale, the Positive and Negative Affect Scale, the satisfaction with life scale and their frequency of participation in daily life activities (social activities, mass communication use, building knowledge, home activities and hobbies, creative activities, activities outside home, community service activities and games). Results frommediational analyses revealed that social activities partially mediated the relationship between personality variables – self-esteem and optimism – and subjective well-being. Implications of these findings for future research on subjective well-being and the role of participation in daily life activities in old age
Prediction of drug-drug interactions of zonisamide metabolism in humans from in vitro data

Objective: The purposes of this study were to identify the P450 enzyme (CYP) responsible for zonisamide metabolism in humans by using expressed human CYPs and to predict drug interaction of zonisamide in vivo from in vitro data. Methods: Ten expressed human CYPs and human liver microsomes were used in the experiments for the identification of enzymes responsible for zonisamide metabolism and for the prediction of drug-drug interactions of zonisamide metabolism in humans from in vitro data, respectively. Two-sulfamoylacetyl phenol, a reductive metabolite of zonisamide, was measured by the HPLC method. Results: From the experiments using ten expressed human CYPs, CYP2C19, CYP3A4 and CYP3A5 were shown to be capable of catalyzing zonisamide reduction. However, an intrinsic clearance, Vmax/kM, of CYP3A4 was much higher than those of CYP2C19 and CYP3A5. From the point of view of enzyme amount in human liver CYPs isoform and their intrinsic clearance, it was suggested that CYP3A4 is mainly responsible for zonisamide metabolism in human CYPs. Zonisamide metabolism in human liver microsomes was markedly inhibited by cyclosporin A, dihydroergotamine, ketoconazole, itraconazole, miconazole and triazolam. We estimated the possibility and degree of change of zonisamide clearance in vivo in clinical dose range from in vitro inhibition constant of other drugs against zonisamide metabolism (Ki) and unbound inhibitor concentration in blood (Iu) in clinical usage. Clearance of zonisamide was maximally estimated to decrease by 31%, 23% and 17% of the clearance without inhibitors i.e. ketoconazole, cyclospolin A and miconazole, respectively. Fluconazole and carbamazepine are estimated to decrease by 5–6% of the clearance of zonisamide. On the other hand, there may be lack of interaction of zonisamide metabolism by dihydroergotamine, itraconazole and triazolam in clinical dose range. Conclusion: We demonstrated that: (1) zonisamide is metabolized by recombinant CYP3A4, CYP2C19 and CYP3A5, (2) the metabolism is inhibited to a variable extent by known CYP3A4/5 substrates and/or inhibitors in human liver microsomes, and (3) in vitro-in vivo predictive calculations suggest that several compounds demonstrating CYP3A4-affinity might cause in vivo drug-drug interactions with zonisamide.
Organizational decision to adopt hospital information system: An empirical investigation in the case of Malaysian public hospitals

OBJECTIVES
This study mainly integrates the mature Technology-Organization-Environment (TOE) framework and recently developed Human-Organization-Technology (HOT) fit model to identify factors that affect the hospital decision in adopting Hospital Information System (HIS).


METHODS
Accordingly, a hybrid Multi-Criteria-Decision-Making (MCDM) model is used to address the dependence relationships of factors with the aid of Analytic Network Processes (ANP) and Decision Making Trial and Evaluation Laboratory (DEMATEL) approaches. The initial model of the study is designed by considering four main dimensions with 13 variables as organizational innovation adoption factors with respect to HIS. By using DEMATEL, the interdependencies strength among the dimensions and variables are tested. The ANP method is then adopted in order to determine the relative importance of the adoption factors, and is used to identify how these factors are weighted and prioritized by the public hospital professionals, who are wholly familiar with the HIS and have years of experience in decision making in hospitals' Information System (IS) department.


RESULTS
The results of this study indicate that from the experts' viewpoint "Perceived Technical Competence" is the most important factor in the Human dimension. In the Technology dimension, the experts agree that the "Relative Advantage" is more important in relation to the other factors. In the Organization dimension, "Hospital Size" is considered more important rather than others. And, in the Environment dimension, according to the experts judgment, "Government Policy" is the most important factor. The results of ANP survey from experts also reveal that the experts in the HIS field believed that these factors should not be overlooked by managers of hospitals and the adoption of HIS is more related to more consideration of these factors. In addition, from the results, it is found that the experts are more concerned about Environment and Technology for the adoption HIS.


CONCLUSIONS
The findings of this study make a novel contribution in the context of healthcare industry that is to improve the decision process of innovation in adoption stage and to help enhance more the diffusion of IS in the hospital setting, which by doing so, can provide plenty of profits to the patient community and the hospitals.
Caloric restriction and the aging process: a critique.

The main objective of this review is to provide an appraisal of the current status of the relationship between energy intake and the life span of animals. The concept that a reduction in food intake, or caloric restriction (CR), retards the aging process, delays the age-associated decline in physiological fitness, and extends the life span of organisms of diverse phylogenetic groups is one of the leading paradigms in gerontology. However, emerging evidence disputes some of the primary tenets of this conception. One disparity is that the CR-related increase in longevity is not universal and may not even be shared among different strains of the same species. A further misgiving is that the control animals, fed ad libitum (AL), become overweight and prone to early onset of diseases and death, and thus may not be the ideal control animals for studies concerned with comparisons of longevity. Reexamination of body weight and longevity data from a study involving over 60,000 mice and rats, conducted by a National Institute on Aging-sponsored project, suggests that CR-related increase in life span of specific genotypes is directly related to the gain in body weight under the AL feeding regimen. Additionally, CR in mammals and "dietary restriction" in organisms such as Drosophila are dissimilar phenomena, albeit they are often presented to be the very same. The latter involves a reduction in yeast rather than caloric intake, which is inconsistent with the notion of a common, conserved mechanism of CR action in different species. Although specific mechanisms by which CR affects longevity are not well understood, existing evidence supports the view that CR increases the life span of those particular genotypes that develop energy imbalance owing to AL feeding. In such groups, CR lowers body temperature, rate of metabolism, and oxidant production and retards the age-related pro-oxidizing shift in the redox state.