Pre-extension demonstration of field pea varieties in selected districts of Bale highlands, Oromiya national regional state, Ethiopia

Pre extension demonstration of improved field pea varieties was conducted in Goba, Sinana and Agarfa districts of Bale zone. The main objective of the study was to demonstrate and evaluate recently released (Weyib) variety along with standard check. The demonstration was under taken on single plot of 10mx10m area for each variety with the spacing of 30cm between rows and recommended seed rate of 75kg/ha and fertilizer rate of 100kg/ha NPS. Mini-field day involving different stakeholders was organized at each respective site. Yield data per plot was recorded and analysed using descriptive statistics, while farmers’ preference to the demonstrated varieties was identified using focused group discussion and summarized using pair wise ranking methods. The demonstration result revealed that Weyib variety performed better than the standard check (Tulu shanan variety) with an average yield of 34.47qt/ha, while that of the standard check was27.26qt/ha. Weyib variety had 17.27% yield advantage over the standard check. Thus, Weyib variety was recommended for further scaling up. Res. Agric., Livest. Fish.8(3): 267-271, December 2021

The Decision-Making Constraints and Processes of Grant Peer Review, and Their Effects on the Review Outcome

When distributing grants, research councils use peer expertise as a guarantee for supporting the best projects. However, there are no clear norms for assessments, and there may be a large variation in what criteria reviewers emphasize – and how they are emphasized. The determinants of peer review may therefore be accidental, in the sense that who reviews what research and how reviews are organized may determine outcomes. This chapter deals with how the review process affects the outcome of grant review. It is a reprint of a study of the multitude of review procedures practiced in The Research Council of Norway (RCN) in the 1990s. While it is outdated as an empirical study of the RCN, it provides some general insights into the dynamics of grant review panels and the effects of different ways of organising the decision-making in the panels. Notably, it is still one of the few in-depth studies of grant review processes based on direct observation of panel meetings and full access to applications and review documents. A central finding is that rating scales and budget restrictions are more important than review guidelines for the kind of criteria applied by the reviewers. The decision-making methods applied by the review panels when ranking proposals are found to have substantial effects on the outcome. Some ranking methods tend to support uncontroversial and safe projects, whereas other methods give better chances for scholarly pluralism and controversial research.

Diagnostic Evaluation of Policy-Gradient-Based Ranking

Learning-to-rank has been intensively studied and has shown significantly increasing values in a wide range of domains, such as web search, recommender systems, dialogue systems, machine translation, and even computational biology, to name a few. In light of recent advances in neural networks, there has been a strong and continuing interest in exploring how to deploy popular techniques, such as reinforcement learning and adversarial learning, to solve ranking problems. However, armed with the aforesaid popular techniques, most studies tend to show how effective a new method is. A comprehensive comparison between techniques and an in-depth analysis of their deficiencies are somehow overlooked. This paper is motivated by the observation that recent ranking methods based on either reinforcement learning or adversarial learning boil down to policy-gradient-based optimization. Based on the widely used benchmark collections with complete information (where relevance labels are known for all items), such as MSLRWEB30K and Yahoo-Set1, we thoroughly investigate the extent to which policy-gradient-based ranking methods are effective. On one hand, we analytically identify the pitfalls of policy-gradient-based ranking. On the other hand, we experimentally compare a wide range of representative methods. The experimental results echo our analysis and show that policy-gradient-based ranking methods are, by a large margin, inferior to many conventional ranking methods. Regardless of whether we use reinforcement learning or adversarial learning, the failures are largely attributable to the gradient estimation based on sampled rankings, which significantly diverge from ideal rankings. In particular, the larger the number of documents per query and the more fine-grained the ground-truth labels, the greater the impact policy-gradient-based ranking suffers. Careful examination of this weakness is highly recommended for developing enhanced methods based on policy gradient.

Selection and Ranking of the Most Suitable Drones for Sustainable Traffic Management Using Multi-Criteria Analysis Approach

The traffic problem is one of the significant issues facing many large cities. So, transportation plans should be analyzed very well. Static cameras are tools and the right solution for traffic monitoring and management. But, nowadays, drones come into prominence as popular, effective, and more sustainable tools in traffic control and have been used for various traffic applications. In this study, a model is proposed for the selection of the most suitable drone under the specific characteristics to ensure a contributor to traffic management efforts. The decision model is structured with AHP and MOORA-TOPSIS and VIKOR ranking methods. The weighting of criteria is carried out by the AHP method, and a combination of AHP and ranking methods are used for the best selection. The results of the analysis were compared using Spearman's rank correlation, and it is seen that the results are at the desired level.

Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification

With the advancements in processing units and easy availability of cloud-based GPU servers, many deep learning-based methods have been proposed for Aspect Level Sentiment Classification (ALSC) literature. With this increase in the number of deep learning methods proposed in ALSC literature, it has become difficult to ascertain the performance difference of one method over the other. To this end, our study provides a statistical comparison of the performance of 35 recent deep learning methods with respect to three performance metrics-Accuracy, Macro F1 score, and Time. The methods are evaluated for eight benchmark datasets. In this study, the statistical comparison is based on Friedman, Nemenyi, and Wilcoxon tests. As per the results of statistical tests, the top-ranking methods could not significantly outperform several other methods in terms of Accuracy and Macro F1 score and performed poorly on-time metric. However, the time taken by any method is crucial to analyze the overall performance. Thus, this study aids the selection of the Deep Learning method, which maximizes the accuracy and Macro F1 score and takes minimal time. Our study also establishes a framework for validating the performance of new and alternate methods in ALSC that can be helpful for researchers and practitioners working in this area.

Methods for Characterizing and Examining Marine Fishery Ecosystems

There are many variables associated with assessing marine fishery ecosystems. These include exploring facets of the living marine resources (LMRs), habitats, oceans, economics, and social considerations associated with marine social-ecological systems. Yet which ones can help track progress toward ecosystem-based fisheries management (EBFM) and, by extension, the efficacy of LMR management? This chapter provides a list of over 90 indicators we will use throughout the regional chapters, with documentation of data sources, time periods, and geographies covered, and the typical caveats associated with these data. This chapter also notes the methodology of how we synthesized all this information across all the regional chapters, noting the appropriate statistical and ranking methods we employed and the benchmarking criteria we considered to ascertain progress toward EBFM.

FUZZY AGGREGATION FOR RANKING IN BUSINESS

Today, businesses depend strongly on data and the opinion of customers or the experience of managers or experts. The large databases contain non-heterogeneous data, which is the ground for further decisions. Business uses multicriterial decisions in more areas (e.g., customer care, marketing, product development, risk management, HR, etc.) and often it is based on assessment. One of the assessment methods is the ranking, which can be done by crisp values of data where the sharp borders between evaluated entities do not give the adequate ranking result. On the other hand, the ranking process is based on the qualitative assessment, which has linguistic expression. It is more familiar and understandable for people. The article shows how to treat non-heterogeneous data to prepare them for a ranking process using fuzzy sets theory. The article aims at offering several types of ranking methods based on different inputs and preferences of the user and describes appropriate fuzzy aggregations for solving the ranking problem.

A novel sensitivity-based method for feature selection

Sensitivity analysis is a popular feature selection approach employed to identify the important features in a dataset. In sensitivity analysis, each input feature is perturbed one-at-a-time and the response of the machine learning model is examined to determine the feature's rank. Note that the existing perturbation techniques may lead to inaccurate feature ranking due to their sensitivity to perturbation parameters. This study proposes a novel approach that involves the perturbation of input features using a complex-step. The implementation of complex-step perturbation in the framework of deep neural networks as a feature selection method is provided in this paper, and its efficacy in determining important features for real-world datasets is demonstrated. Furthermore, the filter-based feature selection methods are employed, and the results obtained from the proposed method are compared. While the results obtained for the classification task indicated that the proposed method outperformed other feature ranking methods, in the case of the regression task, it was found to perform more or less similar to that of other feature ranking methods.

autoFC: An R Package for Automatic Item Pairing in Forced-Choice Test Construction

Recently, there has been increasing interest in adopting the forced-choice (FC) test format in non-cognitive assessments, as it demonstrates faking resistance when well-designed. However, traditional or manual pairing approaches to FC test construction are time- and effort- intensive and often involve insufficient considerations. To address these issues, we developed the new open-source autoFC R package to facilitate automated and optimized item pairing strategies. The autoFC package is intended as a practical tool for FC test constructions. Users can easily obtain automatically optimized FC tests by simply inputting the item characteristics of interest. Customizations are also available for considerations on matching rules and the behaviors of the optimization process. The autoFC package should be of interest to researchers and practitioners constructing FC scales with potentially many metrics to match on and/or many items to pair, essentially exempting users from the burden of manual item pairing and reducing the computational costs and biases induced by simple ranking methods.