The World Bank’s social assistance recommendations for developing and transition countries: Containment of political unrest and mobilisation of political support

This article presents a political-sociological analysis of the World Bank’s social assistance programmes in developing and transition countries. It builds on the argument that political objectives have played a critical role for the Bank in shaping these policies, including the prevention and containment of social unrest as well as mobilization of popular support. The paper presents empirical evidence based on an analysis of 447 World Bank policy recommendation documents published between 1980 and 2013. It was found that, despite the Bank’s denial of having any political agenda, many WB documents explicitly refer to social assistance as a possible instrument for governments to contain social unrest and mobilize political support. Moreover, the World Bank’s political concerns have increased steadily over the last three decades. The findings support the argument that international institutions such as the WB do not solely consider the well-being of people as an end in itself but also as a means of achieving further political goals. This political dimension of social assistance programmes has consequences for the way policy recommendations should be interpreted by political and social actors in developing and transition countries

Context-aware confidence sets for fine-grained product recognition

We present a new approach for fine-grained classification of retail products, which learns and exploits statistical context information about likely product arrangements on shelves, incorporates visual hierarchies across brands, and returns recognition results as “confidence sets” that are guaranteed to contain the true class at a given confidence level. Our system consists of three important components: 1) a nested hierarchy of product classes are automatically constructed based on visual similarities, 2) a confidence set predictor is trained based on class posteriors by using coarse-to-fine binary classifiers to discriminate each nested cluster of the hierarchy from the remainder of classes and a Bayesian network (BN) model that encodes the joint distribution of classifier scores with the fine-level class variable, and 3) n hidden Markov model (HMM) is trained with nested hidden states from the class hierarchy to model spatial transition across the nodes of product class hierarchy and resolve errors in the context-free confidence set results. Novel aspects of the proposed method include 1) combining confidence sets and context information via a HMM, 2) applying this concept to fine grained recognition of products arranged in retail shelves, and 3) presenting experimental results on four large datasets, collected from actual retail stores. We compare our approach with existing confidence set approaches and state-of-the-art convolutional neural networks classifiers including SENet-154, DenseNet-161, B-CNN, and Inception-Resnet-v2. Our approach performs comparably or better than state-of-the-art deep classifiers and exhibits high accuracy for relatively small confidence set sizes

Retail product recognition with a graphical shelf model (Çizgisel raf modeli ile perakende ürün tanıma)

Recently, retail product recognition has become an interesting computer vision research topic. The classification of products on shelves is a very challenging classification problem because many product classes are visually similar in terms of shape, color, texture, and metric size. In shelves, same or similar products are more likely to appear adjacent to each other and displayed in certain arrangements rather than at random. The arrangement of the products on the shelves has a spatial continuity both in brand and metric size. By using this context information, the co-occurrence of the products and the adjacency relations between the products can be statistically modeled. In this work, we present a context-aware hybrid classification system for the problem of fine-grained product class recognition. The proposed hybrid approach improves the accuracy of the context-free image classifiers, by combining them with a probabilistic graphical model based on Hidden Markov Models. The fundamental goal of this paper is to use contextual relationships in retail shelves to improve accuracy of the product classifier

Context-aware hybrid classification system for fine-grained retail product recognition

We present a context-aware hybrid classification system for the problem of fine-grained product class recognition in computer vision. Recently, retail product recognition has become an interesting computer vision research topic. We focus on the classification of products on shelves in a store. This is a very challenging classification problem because many product classes are visually similar in terms of shape, color, texture, and metric size. In shelves, same or similar products are more likely to appear adjacent to each other and displayed in certain arrangements rather than at random. The arrangement of the products on the shelves has a spatial continuity both in brand and metric size. By using this context information, the co-occurrence of the products and the adjacency relations between the products can be statistically modeled. The proposed hybrid approach improves the accuracy of context-free image classifiers such as Support Vector Machines (SVMs), by combining them with a probabilistic graphical model such as Hidden Markov Models (HMMs) or Conditional Random Fields (CRFs). The fundamental goal of this paper is using contextual relationships in retail shelves to improve the classification accuracy by executing a context-aware approach

Automated Extraction of Socio-political Events from News (AESPEN): Workshop and Shared Task Report

We describe our effort on automated extraction of socio-political events from news in the scope of a workshop and a shared task we organized at Language Resources and Evaluation Conference (LREC 2020). We believe the event extraction studies in computational linguistics and social and political sciences should further support each other in order to enable large scale socio-political event information collection across sources, countries, and languages. The event consists of regular research papers and a shared task, which is about event sentence coreference identification (ESCI), tracks. All submissions were reviewed by five members of the program committee. The workshop attracted research papers related to evaluation of machine learning methodologies, language resources, material conflict forecasting, and a shared task participation report in the scope of socio-political event information collection. It has shown us the volume and variety of both the data sources and event information collection approaches related to socio-political events and the need to fill the gap between automated text processing techniques and requirements of social and political sciences

Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022): Workshop and Shared Task Report

We provide a summary of the fifth edition of the CASE workshop that is held in the scope of EMNLP 2022. The workshop consists of regular papers, two keynotes, working papers of shared task participants, and task overview papers. This workshop has been bringing together all aspects of event information collection across technical and social science fields. In addition to the progress in depth, the submission and acceptance of multimodal approaches show the widening of this interdisciplinary research topic.Comment: to appear at CASE 2022 @ EMNLP 202