Better representation learning for TPMS

Avec l’augmentation de la popularité de l’IA et de l’apprentissage automatique, le nombre de participants a explosé dans les conférences AI/ML. Le grand nombre d’articles soumis et la nature évolutive des sujets constituent des défis supplémentaires pour les systèmes d’évaluation par les pairs qui sont cruciaux pour nos communautés scientifiques. Certaines conférences ont évolué vers l’automatisation de l’attribution des examinateurs pour les soumissions, le TPMS [1] étant l’un de ces systèmes existants. Actuellement, TPMS prépare des profils de chercheurs et de soumissions basés sur le contenu, afin de modéliser l’adéquation des paires examinateur-soumission. Dans ce travail, nous explorons différentes approches pour le réglage fin auto-supervisé des transformateurs BERT pour les données des documents de conférence. Nous démontrons quelques nouvelles approches des vues d’augmentation pour l’auto-supervision dans le traitement du langage naturel, qui jusqu’à présent était davantage axée sur les problèmes de vision par ordinateur. Nous utilisons ensuite ces représentations d’articles individuels pour construire un modèle d’expertise qui apprend à combiner la représentation des différents travaux publiés d’un examinateur et à prédire leur pertinence pour l’examen d’un article soumis. Au final, nous montrons que de meilleures représentations individuelles des papiers et une meilleure modélisation de l’expertise conduisent à de meilleures performances dans la tâche de prédiction de l’adéquation de l’examinateur.With the increase in popularity of AI and Machine learning, participation numbers have exploded in AI/ML conferences. The large number of submission papers and the evolving nature of topics constitute additional challenges for peer-review systems that are crucial for our scientific communities. Some conferences have moved towards automating the reviewer assignment for submissions, TPMS [1] being one such existing system. Currently, TPMS prepares content-based profiles of researchers and submission papers, to model the suitability of reviewer-submission pairs. In this work, we explore different approaches to self-supervised fine-tuning of BERT transformers for conference papers data. We demonstrate some new approaches to augmentation views for self-supervision in natural language processing, which till now has been more focused on problems in computer vision. We then use these individual paper representations for building an expertise model which learns to combine the representation of different published works of a reviewer and predict their relevance for reviewing a submission paper. In the end, we show that better individual paper representations and expertise modeling lead to better performance on the reviewer suitability prediction task

The rent's too high: Self-archive for fair online publication costs

The main contributors of scientific knowledge, researchers, generally aim to disseminate their findings far and wide. And yet, publishing companies have largely kept these findings behind a paywall. With digital publication technology markedly reducing cost, this enduring wall seems disproportionate and unjustified; moreover, it has sparked a topical exchange concerning how to modernize academic publishing. This discussion, however, seems to focus on how to compensate major publishers for providing open access through a "pay to publish" model, in turn transferring financial burdens from libraries to authors and their funders. Large publishing companies, including Elsevier, Springer Nature, Wiley, PLoS, and Frontiers, continue to earn exorbitant revenues each year, hundreds of millions of dollars of which now come from processing charges for open-access articles. A less expensive and equally accessible alternative exists: widespread self-archiving of peer-reviewed articles. All we need is awareness of this alternative and the will to employ itComment: 8 pages, 1 figure, 19 reference

Response of Root Properties to Tripartite Symbiosis between Lucerne (Medicago sativa L.), Rhizobia and Mycorrhiza Under Dry Organic Farming Conditions

It is generally considered that root turnover is a major contributor to organic matter and mineral nutrient cycles in organic managed agroecosystems. Approach: This study designed to investigate whether microbial activity could affect on root properties of lucerne in an organically managed field under dry weather conditions. The trial was laid out as a factorial experiment in the fields of the University of Natural Resources and Applied Life Sciences, Vienna-Austria at Raasdorf in 2007. The experimental factors of Sinorhizobium meliloti and arbuscular mycorrhiza (AM) including Glomus etunicatum, G. intraradices and G. claroideum and irrigation levels were tested. Results: Results showed that increasing water deficit affected root dry weigh, specific root mass and root length significantly at 1% level and co-inoculation of rhizobium and mycorrhiza with irrigation could increase all root parameters. Data’s of variance analysis for mycorrhizal colonization showed that main effect of using mycorrhiza had significant effects on root parameters at 5% and 1% probability level in first and second harvest, respectively. Results of mean comparisons by Duncan’s multiple range test showed that mycorrhizal colonization was higher in the inoculated treatments by rhizobium , mycorrhiza and irrigated plots in both harvests. Double interaction of mycorrhiza and irrigation was higher in both harvests (37.05% and 65.73%, respectively). Conclusion: Hence, it can be suggested that the tripartite symbiosis of S. meliloti, AM and lucerne can improve the performance of lucerne in organic farming and under dry conditions. Such traits could be incorporated into breeding programs to improve drought tolerance especially in organic fields

Genetic diversity and distance among Iranian and European alfalfa (Medicago sativa L.) genotypes

Alfalfa is the best known fodder crop with high ability of biological nitrogen fixation and drought tolerance in dry, Pannonian region of east Austria. Different morphological and physiological characteristics of 18 alfalfa genotypes from different geographical origins, 8 Iranian ecotypes and 10 European cultivars were evaluated under irrigated and rainfed conditions during 2006-08 cropping seasons. The objectives of this study were to measure genetic distance and divergence among genotypes and to classify them based on morphological and physiological characters. Cluster analysis differentiated Iranian ecotypes and European cultivars from each other under irrigated condition, and when data averaged across two environments (irrigated and rainfed). However, under rainfed conditions small changes occurred in grouping of genotypes due mainly to differential responses of the genotypes to rainfed condition. Considerable genetic distance observed between Iranian and European genotypes. Different crossing programs are recommended between Iranian and European genotypes to develop new alfalfa cultivars

Post operative sore throat: Comparison between Macintosh versus video laryngoscope in patients intubated by trainee anaesthetists - A randomised control trial

Objectives: Postoperative sore throat (POST) is a common complication related to endotracheal intubation. The aim of this study was to compare the incidence of POST in patients intubated by trainee anaesthetist using Video Laryngoscope™ (VDL) or Conventional Macintosh Laryngoscope (CL).Methods: Total 110 patient scheduled for elective laparoscopic cholecystectomy were included from main operating room of Aga Khan University Hospital between June 2017-2018. The standardized perioperative protocol was used for general anaesthesia. Selected patients were randomly allocated into conventional laryngoscopy (CL) group or video laryngoscopy (VDL) group. The evaluation of sore throat was done at 1st, 12th and 24th hour postoperatively using a ten-point visual analogue scale.Results: The demographic characteristics, including intubation time, related complications or any other maneuver required were similar between the groups. The incidence of POST at 1st hour was 47% patients in CL group and 38% in VDL group (p=0.335). At 12th hour, 34.5% patients in CL and 38% in VDL reported POST (p=0.692). Similarly at 24th hour, 25% patients in CL and 16% in VDL group reported POST (p=0.669).Conclusions: There was no significant difference in incidence of POST for patients intubated by trainee anaesthetists using either CL or VDL. Objective evidence of training and laryngoscope technique can impact of POST

Suitability of drought tolerance indices for selecting alfalfa (Medicago sativa L.) genotypes under organic farming in Austria

In eastern Austria, alfalfa is usually grown as a rainfed crop in crop rotations in organic farming systems, where year-to-year rainfall fluctuations cause different levels of drought stress. To identify the suitability of different alfalfa genotypes and drought tolerance indices, 18 contrasting alfalfa genotypes were evaluated under irrigated and rainfed conditions at the research station of the University of Natural Resources and Life Sciences (BOKU), Vienna, Austria, during 2006-08. The first study year (2006) was considered as the establishment year. Five drought tolerance selection indices were estimated based on shoot dry matter, total biomass yield and biological nitrogen fixation (BNF) data. The correlation between irrigated and rainfed performances increased (from r=-0.17 to 0.56) with decreasing stress intensity from the first to the second year. Genotypes Sitel, Plato ZS, Vlasta and NS-Banat were the best genotypes based on their performance under both conditions. Drought tolerance selection indices TOL and SSI showed high correlations (r = 0.32 to 0.81) only with rainfed performance, and SSI was the index that best identified genotypes with high yield potential under rainfed conditions. Indices STI and GMP were the ones that best identified genotypes with high performance under both conditions

Study of Multi-Classification of Advanced Daily Life Activities on SHIMMER Sensor Dataset

Today the field of wireless sensors have the dominance in almost every person’s daily life. Therefore researchers are exasperating to make these sensors more dynamic, accurate and high performance computational devices as well as small in size, and also in the application area of these small sensors. The wearable sensors are the one type which are used to acquire a person’s behavioral characteristics. The applications of wearable sensors are healthcare, entertainment, fitness, security and military etc. Human activity recognition (HAR) is the one example, where data received from wearable sensors are further processed to identify the activities executed by the individuals. The HAR system can be used in fall detection, fall prevention and also in posture recognition. The recognition of activities is further divided into two categories, the un-supervised learning and the supervised learning. In this paper we first discussed some existing wearable sensors based HAR systems, then briefly described some classifiers (supervised learning) and then the methodology of how we applied the multiple classification techniques using a benchmark data set of the shimmer sensors placed on human body, to recognize the human activity. Our results shows that the methods are exceptionally accurate and efficient in comparison with other classification methods. We also compare the results and analyzed the accuracy of different classifiers

Duration of cessation of smoking before elective surgery: Impact on intraoperative hemodynamics and early postoperative pain in developing country

Aim and Background: It is estimated that up to 20% of patients coming for elective surgery are smokers and carry a risk of perioperative complications. Though smoking cessation and its impact on perioperative outcome are widely investigated worldwide we were unable to find any data in Pakistan. The objective of the study is to determine the impact of the duration of smoking cessation before elective surgery on intraoperative hemodynamics and postoperative pain in Pakistani population. Methods: It was a prospective cohort study conducted at the Aga Khan University Hospital Karachi, Pakistan, for one-year duration. A total of 260 patients scheduled for elective noncardiac surgery under general anaesthesia were recruited. Surgery under regional anaesthesia and minor surgery under general anaesthesia were excluded. Data on self-reported duration of smoking cessation by patients, intraoperative haemodynamics, postoperative pain scores and duration of hospital stay were collected by independently trained data collectors from the preoperative area until the patient is discharged from the hospital. Results: A data from 256 patients were analyzed. On the basis of self-reported duration of preoperative smoking cessation, patients were divided into 4 groups (Group 1: less than 2 days, Group 2: more than 2 days to 7 days, Group 3: more than 7 days to 4 weeks and Group 4: more than 4 weeks). It was found that the longer the duration of cessation of smoking is the less haemodynamic changes and lower postoperative pain scores. Length of stay did not show any difference among all four groups. No major postoperative pulmonary complication was found in any study patient. Conclusions: Duration of cessation of smoking before elective surgery is a significant predictor of intraoperative haemodynamics and early postoperative pain in Pakistani population. A short duration of smoking cessation also helps to avoid some of the adverse effects and reduces perioperative complications

Impulsive Buying Behaviour of Omani Women in Apparel Industry

Human behaviour is always based on impulses towards buying and purchase decisions, it is much observed, extensively researched but still not fully explained. Impulse buying has been the subject of researchers for more than six decades and the research conducted in many parts of the world has followed the evidence results derived from U.S culture. Tuyet Mai et al. (2003), however mentioned that impulsive buying behaviour is a universal phenomenon, which is thought to be influenced by local market conditions, as well as social and cultural forces. Many factors contributed towards impulsive buying behaviours of the purchasers. The objective of this research is to identify, measure and evaluate the  relationship and impact of  internal and external factors on impulsive buying behaviour of Omani women in the apparel industry. The research is exploratory in nature and a verified research instrument was adapted for factors and items. According to the findings two internal factors self-esteem and social desirability and two external factors, price and TV/Media significantly influences the impulsive buying behaviour of the Omani women in the apparel industry. Keywords: Buying Behaviour, Internal & External Factors, Impulsivity, Self Esteem, and Product Promotion. DOI: 10.7176/EJBM/15-2-03 Publication date: January 31st 202