Examination of the Transitions between Modal Representations in Coding Training

This study aims to determine the perceptions of undergraduates, who are receiving coding training in a faculty of education, on modal representations employed in the training process and identify their transition skills between representations. The research used the quantity search method, non-experimental design, and descriptive search models, calculating the obtained data frequencies by numerical analysis. The study was carried out with the participation of 58 undergraduates in the Computer and Instructional Technology Department of an education faculty in the 2018-2019 academic year. The representational skill-testing used in the study consists of 12 open-ended questions developed by the researchers. The reliability of the test was calculated as .96 with the Pearson product-moment correlation coefficient value. Transitions between the representation of mathematics, verbal, flowchart, and code were rankly listed in the test, which was applied in a single session. The obtained data were scored with a grading key and undergraduate achievement was assessed according to the transition between representations. The analysis has revealed that representation transition skills may differ from each other and that coding training, which takes into account these transition skills, should be carried out with flow chart, verbal, mathematical and ultimately code representations, respectively.

On augmenting database schemas by latent visual attributes

Decision-making in our everyday lives is surrounded by visually important information. Fashion, housing, dating, food or travel are just a few examples. At the same time, most commonly used tools for information retrieval operate on relational and text-based search models which are well understood by end users, but unable to directly cover visual information contained in images or videos. Researcher communities have been trying to reveal the semantics of multimedia in the last decades with ever-improving results, dominated by the success of deep learning. However, this does not close the gap to relational retrieval model on its own and often rather solves a very specialized task like assigning one of pre-defined classes to each object within a closed application ecosystem. Retrieval models based on these novel techniques are difficult to integrate in existing application-agnostic environments built around relational databases, and therefore, they are not so widely used in the industry. In this paper, we address the problem of closing the gap between visual information retrieval and relational database model. We propose and formalize a model for discovering candidates for new relational attributes by analysis of available visual content. We design and implement a system architecture supporting the attribute extraction, suggestion and acceptance processes. We apply the solution in the context of e-commerce and show how it can be seamlessly integrated with SQL environments widely used in the industry. At last, we evaluate the system in a user study and discuss the obtained results.

AOL4PS: A Large-Scale Dataset for Personalized Search

Personalized search is a promising way to improve the quality of web search, and it has attracted much attention from both academic and industrial communities. Much of the current related research is based on commercial search engine data, which can not be released publicly for such reasons as privacy protection and information security. This leads to a serious lack of accessible public datasets in this field. The few available datasets though released to the public have not become widely used in academia due to the complexity of the processing process. The lack of datasets together with the difficulties of data processing have brought obstacles to fair comparison and evaluation of personalized search models. In this paper, we constructed a large-scale dataset AOL4PS to evaluate personalized search methods, collected and processed from AOL query logs. We present the complete and detailed data processing and construction process. Specifically, to address the challenges of processing time and storage space demands brought by massive data volumes, we optimized the process of dataset construction and proposed an improved BM25 algorithm. Experiments are performed on AOL4PS with some classic and state-of-the-art personalized search methods, and the experiment results demonstrate that AOL4PS can measure the effect of personalized search models. AOL4PS is publicly available at http://github.com/wanhuaiyu/AOL4PS.

Crystallographic molecular replacement using an in silico-generated search model of SARS-CoV-2 ORF8

The majority of crystal structures are determined by the method of molecular replacement (MR). The range of application of MR is limited mainly by the need for an accurate search model. In most cases, pre-existing experimentally determined structures are used as search models. In favorable cases, ab initio predicted structures have yielded search models adequate for molecular replacement. The ORF8 protein of SARS-CoV-2 represents a challenging case for MR using an ab initio prediction because ORF8 has an all β-sheet fold and few orthologs. We previously determined experimentally the structure of ORF8 using the single anomalous dispersion (SAD) phasing method, having been unable to find an MR solution to the crystallographic phase problem. Following a report of an accurate prediction of the ORF8 structure, we assessed whether the predicted model would have succeeded as an MR search model. A phase problem solution was found, and the resulting structure was refined, yielding structural parameters equivalent to the original experimental solution.

Model of User Data Analysis Complex for the Management of Diverse Web Projects during Crises

This article discusses the relevant task of analyzing user data in the process of managing various web projects. The results of this analysis will help to improve the management of diverse web projects during crises. The authors explore the concept of data heterogeneity in web projects, classify web projects by function and purpose, and analyze the search models and data display in web projects. The proposed algorithms for analyzing user data in the process of managing diverse web projects will improve the structuring and presentation of data on the web project platform. The model user data analysis complex developed by the authors will simplify the process of managing various web projects during crises.

Unemployment Insurance and Job Search Behavior*

How does unemployment insurance (UI) affect unemployed workers’ search behavior? Search models predict that, until benefit exhaustion, UI depresses job search effort and increases reservation wages. Over an unemployment spell, search effort should increase up to benefit exhaustion, and stay high thereafter. Meanwhile, reservation wages should decrease up to benefit exhaustion and stay low thereafter. To test these predictions, we link administrative registers to data on job search behavior from a major online job search platform in France. We follow over 400,000 workers, as long as they remain unemployed. We analyze the changes in search behavior around benefits exhaustion, and take two steps to isolate the individual response to unemployment benefits. First, our longitudinal data allows us to correct for changes in sample composition over the spell. Second, we exploit data on workers eligible for 12–24 months of UI as well as workers ineligible for UI, to control for behavior changes over the unemployment spell that are independent of UI. Our results confirm the predictions of search models. We find that search effort (the number of job applications) increases by at least 50% during the year preceding benefits exhaustion and remains high thereafter. The target monthly wage decreases by at least 2.4% during the year preceding benefits exhaustion, and remains low thereafter. Additionally, we provide evidence for duration dependence: workers decrease the wage they target by 1.5% over each year of unemployment, irrespective of their UI status.

MONEY AND CREDIT REMIX

Money and credit are ubiquitous in actual economies, but there is an active theoretical debate on whether they are both necessary if they can both be used in all transactions. Recently, Gu et al. (2016) have shown that money and credit cannot be simultaneously essential and debt limits do not matter for the determination of real allocations in a class of monetary economies. In this paper, we revisit their irrelevance result in a monetary economy based on Lagos and Wright (2005), which exhibits a misallocation of liquidity that is common in search models of money. We show that monetary loans, which naturally require the use of both money and credit, implement Pareto superior allocations in which the size of debt limits matters.

The Theory and Empirics of the Marriage Market

This article reviews recent developments in the literature on marriage markets. A particular emphasis is put on frameworks based either on frictionless matching models with transfers or on search models.