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Background Scottish Surgical Boot Camps are the induction course for new start Core Surgical Trainees (Residents) in Scotland. The aim is to capitalise on trainees’ receptiveness at a time of transition, and to give them a safe and “flying start.” At their inception, these were the first such courses in the UK to integrate technical and non-technical skills training. As well as knowledge and skills, trainees have learned norms and expectations of a community of professional practice. Methods Over 9 consecutive years of delivery and quality improvement, faculty have learned much about the safe use of simulation, as an adjuvant training modality for a range of competences required in the operating room, the clinic/office, the wards and other clinical domains. Conclusion In this paper we describe the development and content of the course, discuss the importance of the whole “activity system,” and present some evaluation data. We give tips for success, such as using simulations of escalating complexity, and the power of seemingly incidental Learning Outcomes.

With the progress of sensor technologies, quantitative numerical data are available for food and beverage producers. On the other hand, in most cases, consumers evaluate the food and the beverage intuitively and subjectively. It is necessary to clarify how the numerical data correlate with the mass consumer subjective evaluation. Focusing on Japanese Sake, we develop an application to collect the consumer evaluation as numerical data. Also, we propose an evaluation process with the developed application to carry out a tasting experiment. A Japanese Sake tasting experiment has been conducted by the developed application and the proposed process with 22 subjects. The questionnaire opinions from the subjects reveals that VAS (Visual Analog Scales) is appropriate for the input interface of evaluation data in the application, because evaluation of VAS is 3.6 out of 5. Also, it is found that detailed description on the evaluation items of VAS is needed for some items with ambiguous definition.

With the continuous development of science and technology, a huge project of China's primary and secondary school education system, which has attracted worldwide attention, is being carried out in Shenzhou at a dizzying speed. In today's information education, how to do a good job of information education in rural primary and secondary schools is particularly important. This paper puts forward an evaluation model of rural primary and secondary school education informatization based on data sharing. According to the mining results of rural primary and secondary school education informatization evaluation information, the frequent itemsets association rules of rural primary and secondary school education informatization evaluation data are reconstructed, the information of rural primary and secondary school education informatization is extracted, the process optimization control method is used to evaluate the rural primary and secondary school education informatization, and the big data analysis model of rural primary and secondary school education informatization evaluation is constructed combined with descriptive statistical analysis method. The regional linear statistics and process optimization scheduling method are used to realize the evaluation and data sharing design of rural primary and secondary school education informatization. The empirical analysis results show that this method has the advantages of high accuracy, good adaptability, high confidence level, accurate and reliable evaluation results, and improves the evaluation ability of rural primary and secondary school education informatization.

CBR (Case-based Reasoning) is an method in the field of artificial intelligence. This paper describes the application of CBR method in the educational administration system. Five groups of evaluation data were selected during the course of a class,through normalization, fuzzy clustering, and similarity metrics,match the source case with the highest similarity to the target case. The source case consists of 37 students in this class. The target case is a classmate from another class. Students in the target case can use the review plan of the classmates who match the most similar students in the source case. In addition, the intelligent module of the educational administration system is implemented under the jBPM framework. This platform workflow engine can drive the flow of each link, more conducive to intelligence is to achieve.

Aiming at the problem that the index weight distribution of teaching evaluation system is not objective, and the evaluation method cannot feedback the evaluation result in time, a method of constructing a teaching evaluation model based on weighted naive bayes algorithm is proposed. Firstly, a large amount of actual data of students' teaching evaluation was collected and sorted out, and data mining technology was adopted to realize data preprocessing of evaluation data. Secondly, the evaluation value was mapped into different grades, and a classifier based on weighted naive bayes algorithm was established. Finally, the classifier was used to assign different weights to each evaluation index, and the teaching evaluation results could be given in time through the classifier. The experimental results show that compared with the traditional teaching evaluation method, the evaluation model has better classification performance and the classification accuracy can reach 0.75. Therefore, this method can provide a reasonable scientific technical reference for teaching evaluation.

Sound identification may affect sound processing and naming, and then influence the performance in hazard identification and memory behavior. In addition, previous studies have shown that there are many factors that can affect the recognition of sound, including ecological frequencies, typical sounds, and some acoustic properties of sounds [6]. However, there are no data directly explore the impact of the length of presentation time on sound recognition, so we will collect the Naming and evaluation data which is from different category (animal, music, artificial) of 30 familiar sounds by controlling five time periods (150ms, 200ms, 250ms, 300ms, 350ms). For all categories of sound materials, as the presentation time increases, the accuracy of recognition increases significantly. These normative data may be helpful in guiding sound identification in future artificial intelligence studies and help clarify the relationships between sound naming and other variables, including hazard identification.

This paper describes the neural machine translation (NMT) systems of the LIUM Laboratory developed for the French↔German news translation task of the Fourth Conference onMachine Translation (WMT 2019). The chosen language pair is included for the first time in the WMT news translation task. We de-scribe how the training and the evaluation data was created. We also present our participation in the French↔German translation directions using self-attentional Transformer networks with small and big architectures.

To improve the quality of life (QOL) of elderly people, we propose a route planning method considering the subjective evaluation on walkability, safety, and pleasantness of the users. By using this method, it is possible to plan a route with lower physical load, higher safety, and more enjoyable for each elderly user. To quantify their preferences, the acceptable time delay is used for the cost functions. In this study, we confirmed that the factors can take into consideration the mental and physical situation of the user and acquired the quantitative cost functions for these factors. The cost functions were constructed based on the subjective evaluation data. The basic validity of the method was confirmed by a subjective evaluation experiment.

In this study, we sought to identify the factors contributing to the quality of student critical reflections. Prior analyses of institutional assessment and evaluation data, including student reflection scores and experience with critical reflection pedagogies, had shown us that student experience and faculty experience with particular pedagogies were not adequate predictors of students’ ability to articulate their learning through reflective practices. Moreover, we suspected that instructor familiarity with critical reflection would have a much stronger impact than student’s prior experiences. After conducting two focus groups with [authors’ affiliated university] faculty and bolstering our existing literature review, however, we found that instructor and student experience with critical reflection may not impact the quality of a student’s critical reflection as strongly as the way in which reflective prompts were framed. Following subsequent qualitative analyses, the themes of framing, frequency, and feedback emerged, which were then used as a framework to guide the direction of future quantitative analyses. We discuss the implications for the implementation of critical reflection pedagogy and the improvement of student learning outcomes.

The specialty setting and employment guidance in colleges and universities are usually worked out based on empiricism. Data mining technology is rarely used to guide it. In recent years, under the background of Belt and Road Initiative, many schools have offered foreign the majors related to foreign trade English. What is the setting of these majors and the employment satisfaction of graduates? Both have an impact on social and economic development. Access to this information in a timely manner becomes the key. In this paper, the rough set theory, a data mining technology, is used to solve this problem. First of all, this paper collects the employment statistics of foreign trade English graduates from Guangxi Talent International College. Secondly, based on the rough set theory, the quantitative measurement method of college students' employment satisfaction research is put forward, and the intelligent data analysis model based on rough set is established. The experimental results show that the new method is effective and can provide some references for school employment guidance and the training of foreign trade English majors. Finally, the resolution matrix of rough set is used to reduce the attributes of the evaluation data and reduce the unnecessary evaluation indexes. The experimental results show that the evaluation method after the statute is more efficient.

Aiming at the unreasonable weight allocation in traditional teaching evaluation, rough set theory (RST) is applied to the process of teaching evaluation.Using the Teaching Quality evaluation data of Business English Majors from Guangxi Talent International College, we establish the evaluation information system, and calculate the importance of evaluation index, based on which, the weight of index is calculated. Finally, the evaluation results are calculated according to the weight of index. The weight of rough set index is determined entirely by the data itself, avoiding the influence of subjective factors, so it is more scientific, reasonable, objective and fair.

Objective To compare neurocognitive scores between the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) Quick Test (QT) and Online Versions in non-concussed high school athletes. Methods A sample of 47 high school athletes completed the ImPACT Online Version pre-season and the ImPACT QT approximately 3 months later. Paired sample t-tests and Pearson’s correlations examined differences and relationships between the ImPACT batteries. Results The ImPACT QT scores were significantly higher for performance on the Three Letters: Average Counted (p Conclusions Post-injury evaluation data using ImPACT QT should be compared to normative referenced data, and not to pre-season data from the ImPACT Online Version.

Background: Although MRI of the sacroiliac joints (SIJ) is the most sensitive imaging modality for early diagnosis of axial spondyloarthritis (axSpA) it is costly and not readily available. Therefore, clinicians still rely primarily on radiography. The relative degree to which radiography and MRI changes diagnostic ascertainment of axSpA in patients presenting with undiagnosed back pain has not been formally studied. Objectives: We aimed to assess the relative impact of radiography and MRI evaluation on diagnostic ascertainment of axial SpA in patients presenting with undiagnosed back pain to rheumatologists, and the impact of central evaluation. Methods: The multicenter Screening for Axial Spondyloarthritis in Psoriasis, Iritis, and Colitis (SASPIC) Study is aimed at early detection of axial SpA. Consecutive patients ≤45 years of age with ≥3 months undiagnosed back pain with any one of psoriasis, acute anterior uveitis (AAU), or colitis undergo routine clinical evaluation by a rheumatologist for axial SpA and MRI evaluation is ordered per rheumatologist decision. The rheumatologist determines the presence or absence of axial SpA at 3 consecutive stages: 1. After the clinical evaluation; 2. After the results of labs (B27, CRP) and radiography; 3. After the results of MRI evaluation. The eCRF, radiographs, and MRI scans were also assessed centrally. Results: The SASPIC cohort comprises 246 patients (52.4% male, mean age 34.5 years, mean symptom duration 7.3 years, mean back pain duration 7.1 years, B27+ 36.2%) referred with AAU (29.7%), psoriasis (18.7%), Crohn’s colitis (31.3%), ulcerative colitis (20.3%). MRI was conducted in 149 patients. The number of patients diagnosed with axSpA by the local rheumatologist decreased after radiography and then decreased further after MRI while confidence in the diagnosis progressively increased (Table 1). After central evaluation of all patient data, the number of patients diagnosed with axSpA decreased substantially compared to assessment by local readers (Table 2). Conclusion: In a setting of undiagnosed back pain and higher risk for axial SpA, imaging is primarily helpful in ruling out SpA and reducing false positives. Despite this, central evaluation raises concerns regarding ascertainment of false positive SpA in routine practice. Disclosure of Interests: Walter P Maksymowych Grant/research support from: AbbVie, Pfizer, Janssen, Novartis, Consultant for: AbbVie, Eli Lilly, Boehringer, Galapagos, Janssen, Novartis, Pfizer and UCB Pharma; Chief Medical Officer for Canadian Research and Education Arthritis, Raj Carmona Grant/research support from: Amgen, Abbvie, Jannsen, Consultant for: Amgen, Abbvie, BMS, Eli Lilly, Merck, Novartis, Jannsen, Takeda, UCB, James Yeung: None declared, Jon Chan Grant/research support from: Janssen, UCB, Novartis, Pfizer, Celgene, Consultant for: Amgen, Celgene, Eli Lilly, Janssen, Amgen, Abbvie, Novartis, Pfizer, UCB, Sandoz, Merck, Liam Martin: None declared, Sibel Aydin Consultant for: Abbvie, Celgene, UCB, Novartis, Jannsen, Sanofi, Dianne Mosher: None declared, Ariel Masetto Grant/research support from: Amgen, Sanofi, Consultant for: Sanofi, Pfizer, Bristol-Myers Squibb, Novartis, Boehringer Ingelheim, Speakers bureau: Novartis, Stephanie Keeling Consultant for: AbbVie. Pfizer, Eli Lily, Janssen, Amgen, Astrzeeneca, UCB., Olga Ziouzina: None declared, Sherry Rohekar Consultant for: Abbvie, Amgen, BMS, Celgene, Eli-Lilly, Janssen, Merck, Novartis, Pfizer, Roche, Sanofi, UCB, Joel Paschke: None declared, Amanda Carapellucci: None declared, Robert G Lambert Consultant for: Bioclinica, Parexel, Abbvie

Lung cancer is one of the most common and fatal types of cancer, and pulmonary nodule detection plays a crucial role in the screening and diagnosis of this disease. A well-trained deep neural network model can help doctors to find nodules on computed tomography(CT) images while requiring lots of labeled data. However, currently available annotating systems are not suitable for annotating pulmonary nodules in CT images. We propose a web-based lung nodules annotating system named as DeepLNAnno. DeepLNAnno has a unique three-tier working process and loads of features like semi-automatic annotation, which not only make it much easier for doctors to annotate compared to some other annotating systems but also increase the accuracy of the labels. We invited a medical group from West China Hospital to annotate the CT images using our DeepLNAnno system, and collected a large number of labeled data. The results of our experiments demonstrated that a usable nodule-detection system is developed, and good benchmark scores on our evaluation data are obtained.

© 2019, © 2019 AMEE. Background: Recent global increases in medical student numbers and shifts in medical education from teaching hospitals to community settings call for effective strategies to meet the demand for general practice teaching placements. It has been proposed that “multi-level learning” (MLL), in which learning and teaching are shared across different levels of learners, may provide teaching efficiencies and valuable experiences for learners and teachers. Aims: To identify, evaluate and synthesize the evidence related to the types, benefits, challenges, and facilitators of MLL in community-based general practice, and the underlying mechanisms and associated contexts to explain the reported outcomes. Method: A realist synthesis approach guided the systematic review. Results: Fifteen papers were identified, providing primary evaluation data predominantly from interviews with or surveys of key stakeholders. Generally, all levels of learners reported overall satisfaction with their MLL experiences. Medical students appreciated learning from prevocational doctors and registrars due to social and cognitive congruence. Mechanisms and contexts that supported our hypotheses regarding successful MLL outcomes were identified, with “a strong teaching culture” being a major mechanism. Conclusions: The findings can help inform practices considering the implementation or enhancement of MLL initiatives in general practice. Further research should include measuring defined learning outcomes.

Australian Aboriginal and Torres Strait Islander people experience disproportionately higher rates of sexually transmissible infections (STIs) and blood borne viruses (BBVs) when compared with the non-Indigenous population. Both incidence and prevalence data for bacterial STIs, such as chlamydia, gonorrhea, trichomonas, and syphilis in remote areas of Australia are reported at rates many times higher than that of non-Indigenous Australians. Similarly, rates of hepatitis B are disproportionately higher for non-Indigenous people in remote communities. The Young Deadly STI and BBV Free project was designed to increase the uptake of STI and BBV testing and treatment in young Aboriginal and Torres Strait Islander people living in remote and very remote areas of South Australia, Western Australia, Queensland, and the Northern Territory. Peer education formed one component of this pilot project and involved training up to 100 young Aboriginal and Torres Strait Islander people across 19 communities in a culturally appropriate and respectful manner on the transmission, testing, and treatment of STIs and BBVs. The trained peer educators were then required to deliver three community education sessions to young people in their respective communities in an effort to raise awareness about STIs and BBVs and encourage testing and treatment uptake. Preliminary evaluation findings, limited to the trained peer educators, revealed the peer educator training program contributed to STI and BBV knowledge gains among the trained peer educators and positively influenced their behavioral intentions and attitudes pertaining to STIs and BBVs. Working with remote Aboriginal and Torres Strait Islander populations on a highly sensitive, stigmatized topic presented many methodological challenges, particularly in terms of ensuring the collection of reliable evaluation data across geographically remote communities. The challenges and strengths associated with the implementation of the peer education training program along with implications for developing culturally inclusive evaluation practices will be discussed.

International audience; This paper presents a methodology for the evaluation of network services security and the security of protection products. This type of evaluation is an important activity, considering the ever-increasing number of security incidents in networks. Those evaluations can present different challenges with a variety of properties to verify and an even larger number of tools available to compose and orchestrate together. The chosen approach in the paper is to simulate scenarios to perform traffic generation containing both benign and malicious actions against services and security products, that can be used separately or conjointly in attack simulations. We use our recently proposed method to generate evaluation data. This methodology highlights the preparation efforts from the evaluator to choose an appropriate data generating function and make topology choices. The paper presents the case and discusses the experimental results of an evaluation of a network-based IDS, with only benign traffic, only malicious traffic, and mixed traffic

Network intrusion detection system (NIDS) monitors network traffic to detect an unauthorized activity in computer networks. The NIDS is classified according to detection technique into signature and anomaly based. Each of them has its own advantage and disadvantage. The signature-based is more effective in detecting known attacks but it is unable to detect new attacks. The anomaly-based is better in detecting new attacks but it may produce many false alarms. NIDS which use both of them try to exploit the strengths of them. In this paper we propose an improvement of the well known Snort NIDS using clonal selection algorithm (CSA). The proposed approach is evaluated using the 1999 DARPA Intrusion Detection Evaluation Data Sets of MIT (Massachusetts Institute of Technology) as a testbed. The conducted experiments compare the recall, precision, and F-score of Snort NIDS on its own, Snort NIDS improved by negative selection algorithm (NSA), and the proposed approach. The obtained results show that the proposed approach is more powerful than the others.

The quince fruit can be an alternative for producing healthy tea because it is a rich source of bioactive compounds. This study was conducted to evaluate the effects of heating (for 20, 40 and 60 min) on the bioactive compounds of quince and quality attributes its tisanes. To this aim, phenolic content of the treated sample (for 40 min according to sensory analysis) was evaluated by HPLC and antioxidant activity was measured using DPPH method. The analysis of sensory evaluation data demonstrated that the heating process significantly improved the color and taste of the quince tisanes. Thermal treatment (180 °C) for 40 min had maximum acceptability. Although, results confirmed negative effects of the heating process on phenolic content, it showed an increase in antioxidant activity. The HPLC data displayed significant reduction in total amount of five identified phenolic compounds from 460.51 to 25.49 mg/25 g extract by heat treatment. In contrast the roasted sample exhibited lower IC50 (663.88 µg/ml) than the unprocessed sample (1098.66 µg/ml). Considering the elevated antioxidant activity achieved through the heating process, the quince tisane can be considered as a healthy beverage.

In September 2015, Germany witnessed an unanticipated migration of refugees toward the European Union. The government established an open-border policy that meant Germany would harbor all refugee arrivals. In large, the civil society joined efforts to create a so-called Willkommenskultur (welcome culture) during the “summer of welcome.” This chapter will introduce the project “Start ins Deutsche” (German language kick-off) of Goethe University Frankfurt as an ambitious example of civil society initiatives. Start ins Deutsche was founded on the premise of “integration by language learning.” Within Start ins Deutsche, university students volunteer to teach German to refugees. In many cases these refugees have a realistic perspective to enroll into fulltime studies at Goethe University at a later stage to pursue academic degrees. In this chapter, the authors outline the project and its main aims. Based on this, the authors thereafter analyze evaluation data about Start ins Deutsche with regard to the perceptions of German language teachers and their language learners, respectively. The evaluation data of Start ins Deutsche reveal that the German language teachers interpret their role beyond being just teachers, while the learners appreciate the effort of their teachers in every aspect. Hence, the authors believe the project serves as a best-practice example for a civil society project toward establishing a Willkommenskultur in Germany.

Bird Sound Detection (BSD) is helpful for monitoring biodiversity and in this regard, deep learning networks have shown good performance in BSD in recent years. However, such a complex network structure requires high memory resources and computing power at great cost for performing the extensive calculations required, which make it difficult to implement the hardware in BSD. Therefore, we designed an audio classification method for BSD using a Binarized Convolutional Neural Network (BCNN). The convolutional layers and fully connected layers of the original Convolutional Neural Network were binarized to two values. The Area Under ROC Curve (AUC) score of BCNN achieved comparable results with the CNN in an unseen evaluation. This paper proposes two networks (CNNs and BCNNs) for the BSD task of the IEEE AASP Challenge on the Detection and Classification of Acoustic Scenes and Events (DCASE2018). The Area Under ROC Curve (AUC) score of BCNN achieved comparable results with CNN on the unseen evaluation data. More importantly, the use of the BCNN could reduce the memory requirement and the hardware loss unit, which are of great significance to the hardware implementation of a bird sound detection system.

Computer science courses, like many others, encompass subjects that are difficult to understand for students. Reflection on these subjects seems to be important for gaining a better understanding of complex and fundamental concepts in informatics. This paper presents a didactical setting that aims at fostering understanding of such hard subjects. In particular, the evolution of this didactical setting started out from combining ten-minute papers, peer review, and bonus scores. The combination of these didactical elements has been employed in an introductory computer science course that covers, among other things, several topics from theoretical computer science. The paper presents the detailed setup of the learning setting and its underlying goals as well as how it evolved over several years. Furthermore, the paper presents a qualitative and quantitative evaluation of the approach, both from the perspective of students and the perspective of instructors. Evaluation also yields data that expose differences between different versions of the approach. Furthermore, evaluation data also support a critical analysis of potential success and risk factors with respect to the efficacy of the approach, in particular the type of problem statement, the motivation for providing proper feedback, and the role of bonus scores. Overall, the learning setting yields encouraging results, yet offers various options for refinements in future work.

Inter-Annotator Agreement (IAA) is used as a means of assessing the quality of NLG evaluation data, in particular, its reliability. According to existing scales of IAA interpretation – see, for example, Lommel et al. (2014), Liu et al. (2016), Sedoc et al. (2018) and Amidei et al. (2018a) – most data collected for NLG evaluation fail the reliability test. We confirmed this trend by analysing papers published over the last 10 years in NLG-specific conferences (in total 135 papers that included some sort of human evaluation study). Following Sampson and Babarczy (2008), Lommel et al. (2014), Joshi et al. (2016) and Amidei et al. (2018b), such phenomena can be explained in terms of irreducible human language variability. Using three case studies, we show the limits of considering IAA as the only criterion for checking evaluation reliability. Given human language variability, we propose that for human evaluation of NLG, correlation coefficients and agreement coefficients should be used together to obtain a better assessment of the evaluation data reliability. This is illustrated using the three case studies.

A nonprofit that undertakes the task of change is making a serious commitment to its improvement, development, and perhaps even longevity and survival. For this reason, it is prudent for organization leaders to methodically assess the efficacy of change efforts and determine the extent to which they have enabled the achievement of organizational goals. This process—evaluation—asks whether a change effort worked the way it was intended to work, and whether it had the intended effect. It also asks how and why things turned out the way they did. In this chapter, we begin with a brief coverage of the history and evolution of evaluation, the purposes of evaluation, who does evaluation, and the various kinds of evaluation that can be conducted. Then we present some common as well as contemporary evaluation approaches, models, and taxonomies, including the familiar and classic Kirkpatrick Four-Level Evaluation Model. Finally, we discuss the nuts and bolts of conducting high-quality evaluation, including designing evaluation, collecting evaluation data, analyzing evaluation data, communicating evaluation results to relevant stakeholders, and evaluating the evaluation process.

This paper presents a comparison of three neural network frameworks used to make volumetric counts in an automated and continuous way. In addition to cars, the application count pedestrians. Frameworks used are: SSD Mobilenet retrained, SSD Mobilenet pre-trained, and GoogLeNet pre-trained. The evaluation data set has a total duration of 60 minutes and comes from three different cameras. Images from the real deployment videos are included when training to enrich the detectable cases. Traditional detection models applied to vehicle counting systems usually provide high values for cars seen from the front. However, when the observer or camera is on the side, some models have lower detection and classification values. A new data set with fewer classes reach similar performance values as trained methods with default data sets. Results show that for the class cars, recall and precision values are 0.97 and 0.90 respectively in the best case, making use of a trained model by default, while for the class people the use of a re-trained model provides better results with precision and recall values of 1 and 0.82.

The Pulsar Search Collaboratory (PSC) is a collaboration between the Green Bank Observatory and West Virginia University, funded by the National Science Foundation. The PSC program is currently expanding nationwide and engages high school students, teachers, and undergraduate mentors in real-world research by searching for pulsars in data collected with the 100-m Green Bank Telescope. In the process, students learn about observational radio astronomy, radio frequency interference, pulsar timing, and data analysis procedures. The primary goals of the PSC are to stimulate student interest in Science, Technology, Engineering, and Mathematics (STEM) careers, to prepare teachers in implementing authentic research with students by training them within a professional scientific community, and to promote student use of information technologies through online activities and workshops. In this paper, we provide an overview of pulsar science and the data analysis students undertake, as well as a general overview of the program. We then discuss evaluation data collected from participants through a series of survey questions to determine if the program's initial goals were met. The program had a positive impact on the students according to multiple measures, in particular, on their understanding of the nature of scientific inquiry and motivation to pursue STEM career paths., Comment: 10 pages, 3 figures, Accepted for publication in American Journal of Physics

This paper introduces the evaluation procedure, evaluation data, participants and evaluation results of 2019 (15th) China Conference on Machine Translation (CCMT 2019) evaluation campaign. Compared with the last evaluation campaign (CWMT 2018), CCMT 2019 MT evaluation is characterized as follows: a new speech translation task is fulfilled; the translation quality estimation task is augmented with a word level track in addition to the sentence level track. Meanwhile, CCMT 2019 receives increases in the number of participants and systems submitted. This paper presents the anonymous evaluation results of all tasks, with a brief summarization of the techniques applied in this evaluation campaign.

We construct Global Voices, a multilingual dataset for evaluating cross-lingual summarization methods. We extract social-network descriptions of Global Voices news articles to cheaply collect evaluation data for into-English and from-English summarization in 15 languages. Especially, for the into-English summarization task, we crowd-source a high-quality evaluation dataset based on guidelines that emphasize accuracy, coverage, and understandability. To ensure the quality of this dataset, we collect human ratings to filter out bad summaries, and conduct a survey on humans, which shows that the remaining summaries are preferred over the social-network summaries. We study the effect of translation quality in cross-lingual summarization, comparing a translate-then-summarize approach with several baselines. Our results highlight the limitations of the ROUGE metric that are overlooked in monolingual summarization. Our dataset is available for download at https://forms.gle/gpkJDT6RJWHM1Ztz9 ., Comment: NewSum workshop at EMNLP 2019, 8 pages

Data mining is to find correlations and rules which ensure meaningful and potentially useful estimations to be carried out for future among vast amount of existing data through computer programs. Today, many commercial or open source software tools are used regarding this matter. In this study, Classfication Analysis comparisons were carried out over the car evaluation data set consisting of 1728 registers especially on Weka, Orange, KNIME and Tanagra as open source software tools.

Cannabidiol (CBD) and Δ9-tetrahydrocannabinol (Δ9-THC) are considered as the most interesting cannabinoids in Cannabis sativa L. for the clinical practice. Since 2013, the Italian law allows pharmacists to prepare and dispense cannabis extracts to patients under medical prescription, and requires the evaluation of CBD and Δ9-THC content in cannabis extracts before sale. Cannabis olive oil extracts are prepared from dried female cannabis inflorescences, but a standard protocol is still missing. In this study, a fast RP-HPLC/UV method has been developed to quantify CBD and Δ9-THC in cannabis olive oil extracts. The analytical quality by design strategy has been applied to the method development, setting critical resolution and total analysis time as critical method attributes (CMAs), and selecting column temperature, buffer pH and flow rate as critical method parameters. Information from Doehlert Design in response surface methodology combined to Monte-Carlo simulations led to draw the risk of failure maps and to identify the method operable design region. The method was validated according to the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines and then implemented in routine analysis. A control strategy based on system control charts was planned to monitor the developed method performances. Evaluation data were recorded over a period of one year of routine use, and both the CMAs showed values within the specifications in every analysis performed. Hence, a new risk evaluation for the future performances of the method was achieved by using a Bayesian approach based on the routine use data, computing the future distribution of the two CMAs. Finally, a study focusing on the monitoring of CBD and Δ9-THC concentrations in cannabis olive oil extracts was carried out. The developed method was applied to 459 extracts. The statistical analysis of the obtained results highlighted a wide variability in terms of concentrations among different samples from the same starting typology of cannabis, underlining the compelling need of a standardised procedure to harmonise the preparation of the extracts.

Explaining recommendations helps users to make more accurate and effective decisions and improves system credibility and transparency. Current explainable recommender systems tend to provide fixed statements such as ”customers who purchased this item also purchased....”. This explanation is generated only on the basis of the purchase history of similar customers, so it does not include the preferences of customers who have purchased the item or a description of the item. Since user-generated reviews generally contain information about the reviewer’s preferences and a description of the item, such reviews typically have more effect on purchase decisions. Therefore, using reviews to explain recommendations should be more useful than providing only a fixed statement explanation. Aiming to create a system that provides personalized explanations for recommendations, we have developed a recurrent neural network model that uses multicriteria evaluation data to generate reviews.

Resumo: Moreira (2005, 2008) e Moreira e Alkmim (2013) observam que a forma senhor e suas variantes de genero e numero sao frequentemente utilizadas como vocativo em construcoes extraidas de pecas teatrais escritas por autores mineiros nos seculos XIX e XX. Considerando os trabalhos citados, a nossa hipotese e a de que a incidencia do uso da forma senhor como vocativo em posicao final resultou em um processo de gramaticalizacao, que originou as formas reduzidas desse item: sinho, seu, sio, sor e so. Neste artigo, a partir de dados de intuicao e da avaliacao de falantes nativos, objetiva-se descrever e analisar as construcoes em que o item so se realiza. Seguimos Hill (2007, 2014) que situa o vocativo na estrutura sintatica, considerando a funcao pragmatica que este item desempenha. Como resultado, tem-se as configuracoes arboreas correspondentes as construcoes em que o item so se realiza e consideracoes sobre o vocativo na estrutura sintatica do dialeto mineiro. Palavras-chave : senhor ; so ; vocativo; estrutura sintatica; funcao pragmatica. Abstract: Moreira (2005, 2008) and Moreira and Alkmim (2013) observe that the form senhor and number and gender and number variants are frequently used as a vocative in constructions taken from plays written by authors from Minas Gerais in the 19th and 20th centuries. Considering the works cited above, our hypothesis is that the incidence of the use of the form senhor as a vocative in final position resulted in a process of grammaticalization that originated the reduced forms of this item: sinho , seu, sio , sor e so. In this article, based on intuition and evaluation data from native speakers, we aim to describe and analyze the constructions in which the item so is realized. We follow Hill (2007, 2014), who places the vocative in the syntactic structure, considering the pragmatic function that this item performs. As a result, we present tree configurations corresponding to the constructions in which the item so is realized, as well as considerations about the vocative in the syntactic structure of the dialect of Minas Gerais. Keywords : senhor ; so ; vocative; syntactic structure; pragmatic function.

Benefit classification analysis is an important content for not only studying the benefit categories of different blocks in Oilfield, but also controlling and adjusting the negative benefit blocks. Due to the lack of comprehensive consideration of development stage and geological conditions, the classification of oil field development units has some limitations. Through comprehensive analysis of the influencing factors of oilfield development benefit, this paper classifies unit development by using system clustering method. DEA (Data Envelopment Analysis) model is used to analyze the relative validity of different development units. In the same geological and development condition units, the method of benefit classification based on validity analysis is used to realize the comprehensive classification of development and economy. Benefit classification analysis using the benefit evaluation data of a low permeability development unit in eastern oilfield shows that it can play a guiding role in benefit classification, negative effect management and operation strategy of the development unit.

Neste artigo apresenta-se os resultados da avaliação de um curso de formação em gestão para diretores de escolas públicas. Uma pesquisa analisou em que medida os egressos desenvolveram as competências de gestão necessária ao exercício do cargo, percebidas a partir das suas práticas de gestão na escola. Foi realizada uma pesquisa e utilização de análise fatorial nas 650 respostas válidas. Os resultados ratificam a literatura sobre os benefícios da formação em gestão. Entretanto, enquanto internacionalmente o uso de dados das avaliações é percebido como parte da gestão dos processos de ensino e aprendizagem, no Brasil os gestores das escolas públicas percebem as práticas relacionadas ao uso desses dados de avaliação como um processo a parte da gestão da escola.Palavras-chave: formação em gestão; gestor de escola pública; avaliação de eficácia; avaliação do domínio.

Dust-lead monitoring studies typically report values as either dust-lead loadings (μg/ft2) or as dust-lead concentrations (μg/g). This study presents an approach for estimating dust-lead concentrations, when only dust-lead loading values are available. A literature search identified five large studies (>200 homes) that contained original data for both dust-lead loading and dust-lead concentration, and could be used to develop an empirically-based loading to concentration (LTC) model. The R2 values, standard error of the regression, slope, and intercept were improved when these original data sets were refined and pooled together. An additional thirty-two studies had summary statistics available for both dust-lead loading and dust-lead concentration, which enabled an independent evaluation of the LTC model. Despite differences in study design, sampling method, and analytical procedures, the LTC model made consistent predictions across studies. Reported central-tendency summary statistics from the independent evaluation data sets showed that measured central tendency dust-lead concentrations from these studies were similar to predicted dust-lead concentrations using the LTC model. Across 142 paired dust-lead loading and dust-lead concentration values from the evaluation datasets, slightly more than 90% of estimated dust-lead concentration values fell within a 90% prediction interval. These results indicate that, when only dust-lead loading values are available, the LTC model provides a reasonable approach for estimating dust-lead concentrations. A limitation of this approach is the greater uncertainty at high dust-lead loadings, caused by variability in total dust loadings (g/ft2) that result in one dust-lead loading value being linked to a range of dust-lead concentration values.

Electrochemical gas sensors (ECGS) have gained substantial popularity in ambient measurements. Several data correction algorithms had been proposed to tackle the drifting response of ECGS due to environmental factors, but there is a lack of performance evaluation of these data correction schemes. To fill this knowledge gap, we conduct a comprehensive evaluation of these data correction algorithms using a large dataset from field comparisons. The dataset covered three commonly used gas pollutants, including CO, NO2 and O3 measured by both ECGS and reference instruments, with a time resolution of 1 min and a duration of 6 months. Taking advantage of this large dataset, the performance of 8 different data correction schemes (2 new algorithms and 6 algorithms from the literature) was benchmarked by a set of evaluation metrics using raw signals from ECGS (nA level currents from the working and auxiliary electrodes). Eight scenarios were considered to examine the robustness of correction algorithms in response to different training and evaluation data period configurations. In addition, the bias dependence on temperature, RH, target gas levels and cross-sensitivity by different correction algorithms was investigated. Recommendations on data correction scheme selection are provided based on the comparison results.

Summary In emergency departments (EDs), demand for care often exceeds the available resources. Triage addresses this problem by sorting patients into categories of urgency. The Interagency Integrated Triage Tool (IITT) is a novel triage system designed for resource-limited emergency care (EC) settings. The system was piloted by two EDs in Papua New Guinea as part of an EC capacity development program. Implementation involved a five-hour teaching program for all ED staff, complemented by training resources including flowcharts and reference guides. Clinical redesign helped optimise flow and infrastructure, and development of simple electronic registries enabled data collection. Local champions were identified, and experienced EC clinicians from Australia acted as mentors during system roll-out. Evaluation data suggests the IITT, and the associated change management process, have high levels of acceptance amongst staff. Subject to validation, the IITT may be relevant to other resource-limited EC settings.

Detailed re-examination of the datasets that were used for a meta-analysis of fishway attraction and passage revealed a number of errors that we addressed and corrected. We subsequently re-analysed the revised dataset, and results showed no significant changes in the primary conclusions of the original study; for most species, effective performance cannot be assured for any fishway type.

This article explores whether child-led research (CLR) benefits the well-being of the children and young people involved. The article draws upon evaluation data from a pilot CLR programme facilitated by a non-government organisation that supports disadvantaged children, young people and families. Nine participants (aged 10–14 years old) volunteered for the CLR programme, conducted over 18 weeks, which involved learning about and undertaking their own research on a topic of their choosing. The programme was externally evaluated both in terms of process and outcomes so as to gain insights into what is involved in CLR and any potential benefits it might offer for participants. The discussion explores a range of positive benefits for children whilst also signalling the challenges involved in planning, implementing and evaluating such initiatives, not least of which is the difficulty in scaling up this kind of research, given its resource-intensive nature.

Undergraduate research experiences may increase persistence in STEM majors. We describe a research program that targets first-year students selected for their curiosity and attitudes towards science. We explain the implementation of the program over 3 years and present evaluation data using a group of matched controls. Participants and controls pursued STEM degrees at equivalent rates, but participants were significantly more involved in research. Initial laboratory interest and mentor pairing may have played a role in this finding. Female participants, particularly those with male laboratory mentors, engaged in more research than men.

Past experimental research has shown that women are penalized with harsh evaluations when they violate gender prescriptions to be nurturing and helpful. Instructor participation in an asynchronous online discussion forum and end-of-class evaluation data from 360 courses was used to test the hypothesis that students would penalize female, but not male, online instructors based on their classroom engagement. Results showed a penalties effect in student ratings for low-participating female, but not male, instructors in gender-balanced courses. The results demonstrate the differential impact of instructor engagement on male and female evaluations, shedding light on when and why gender bias is found in student evaluations. Implications for the use of student evaluations of faculty are discussed.

There has been an increasing interest in patient-centered assessment of psychological treatments. This article reviews the existing patient-generated measures (PGMs) that have been used by clinicians and psychotherapy researchers to collect evaluation data from the patient perspective. A systematic review of literature was performed to identify PGMs in empirical studies between 1990 and 2014. Twenty tools were identified, of which three were designed to assess the outcome and 17 to assess the process of therapy. The characteristics of each are described and discussed, including psychometric data and evidence of clinical utility. This review helps professionals and researchers to implement the recommendation of health policies that advocate the importance of patient-centered care.

Research shows that teachers interpret small differences in student evaluations of teaching as meaningful even when available statistical information indicates that the differences are not reliable. The current research explored the effect of statistical training on college teachers’ tendency to over-interpret student evaluation differences. A sample of 225 English, mathematics and psychology teachers evaluated statistical reports of student evaluation data corresponding to a fictional teacher’s courses. Teachers from the three disciplines varied significantly in their statistics training, but there were no differences in their tendency to rate small, non-significant differences in student evaluations as indicative of the need to make improvements in a course. These results illustrate that statistical knowledge alone does not prevent the over-interpretation of student evaluation means.

This study presents a method of evaluating the crop environmental cost of enterprise based on Kendall W coefficient. The Kendall W coefficient is used to analyze and evaluate the internal evaluation index of environmental cost. We selected some key enterprises of cleaner production auditing of Baoding city, Hebei province in 2014 as the research object and randomly selected nine enterprise cleaner production evaluation data, on the basis of using cleaner production evaluation method based on Kendall W coefficient on the state of enterprise cleaner production assessment analysis and exploration. The calculation results show that the introduction of Kendall W coefficient can improve the accuracy and scientific nature of enterprise’s level evaluation of comprehensive analysis. It is an effective method for the evaluation of environmental cost internalization and it can provide scientific basis for the government to provide financial subsidies to enterprises with better crop environmental cost.