Ranking

A ranking is a relationship between a set of items such that, for any two items, the first is either "ranked higher than", "ranked lower than", or "ranked equal to" the second. In mathematics, this is known as a weak order or total preorder of objects. It is not necessarily a total order of objects because two different objects can have the same ranking. The rankings themselves are totally ordered. For example, materials are totally preordered by hardness, while degrees of hardness are totally ordered. If two items are the same in rank it is considered a tie. By reducing detailed measures to a sequence of ordinal numbers, rankings make it possible to evaluate complex information according to certain criteria. Thus, for example, an Internet search engine may rank the pages it finds according to an estimation of their relevance, making it possible for the user quickly to select the pages they are likely to want to see. Analysis of data obtained by ranking commonly requires non-parame

Quantum-based approach for laboratory risk assessment and decision-making

Ruoxing Liao

With the rising focus on academic safety, there has been an effort to improve the academic safety climate and develop lab-specific risk assessment tools. Despite the progress made in recent years, there is still a deficit of reliable data statistics on safety events and risk assessment in academia, making it difficult to perform full-scale analysis. Moreover, the rapid turnover of personnel and the highly dynamic research tasks in a laboratory environment impede the implementation of classical and well-established risk management strategies, which are usually time-consuming and resource-intensive. Laboratory safety management is always challenging due to lack of data and effective methods. The end of safety management is usually the selection of risk mitigation measures. In the laboratory risk management process, decisions are often made through discussion and compromise. In this regard, it would be beneficial to provide a tool that can support in resource allocation and prioritization of actions. The goal of this project is to develop a decision support tool to assist in the selection of optimal and appropriate preventive or corrective measures in the lab safety management process. The use of decision tools such as Multi-Criteria Decision Making (MCDM) can effectively abstract complex laboratory management problems into a decision framework. This study focuses on the ranking process of competitive corrective measures. Combining the results of interviews and questionnaires, five evaluation criteria were proposed for the normative process of multi-criteria decision making. They are: Residual Risk, Risk reduction, Personnel reliability, Technical reliability, and Cost. classical methods TOPSIS and WSM have been evaluated during the study, and emerging quantum decision making methods have been explored. All three methods are used to simulate the laboratory safety decision making process, and they all present good consistency. Different weighting methods have also been applied to the model and two case studies have been conducted. The first case study is designed to construct the model and verify its feasibility, and the second model is used to investigate the process of group decision making. By applying different MCDM methods, the obtained results are discussed and compared to verify the stability and accuracy of the model. The approach proposed in this research is a promising tool. It offers the possibility to be compatible with different MCDM methods and risk assessment methods, and the proposed decision model could be used as a decision support system to help lab managers make better decisions, or it can provide a neutral point of view as an aid. Future work may include fine-tuning the model. In the following stage, decision criteria should be precisely defined, and corresponding quantitative formulas should be proposed to serve the flexible and dynamic laboratory environment.

2022

Distributed link-based ranking in P2P Web retrieval

Jie Wu

One of the main differences between modern search engines and traditional ones is the adoption of link-based ranking algorithm in ordering Web documents. Google has claimed that it is its link-based ranking algorithm, PageRank that has made the quality of its search results superior. However, existing link-based ranking algorithms are mostly logically centralized although techniques of distributed data management and load balancing have been used in search engines to improve the performance and availability of the systems. With the scale of the Web becoming larger and larger, the scalability of traditional search engines is very much limited by the current centralized algorithms, models, and architectures. The centralization in link-based ranking computation has brought in several critical problems that are hard to solve: centralized bottleneck and single point of failure, prohibitively high computation cost, lag in availability of fresh information, etc. In response to these problems, we have explored several approaches to address them: incremental computation, new distributed model based algorithms, and a common decentralized framework. In particular, we have proposed the new distributed approach based on a Layered Markov Model to distinguish stochastic transitions among Web sites and Web documents. Based on this model, we propose two different methods for computation of ranking of Web documents, a centralized one and a decentralized one. Both produce a well-defined ranking for a given Web graph. We then formally prove that the two approaches are equivalent. This provides a theoretical foundation for decomposing link-based rank computation and makes the computation for a Web-scale graph feasible in a decentralized fashion, such as required for Web search engines having a Peer-to-Peer (P2P) architecture. Furthermore, personalized rankings can be produced by adapting the computation at both the local layer and the global layer. The empirical results show that the ranking generated by our model is qualitatively comparable to or even better than the ranking produced by PageRank. On the other hand, the common framework for Peer-to-Peer Information Retrieval (IR) is situated in a more general scope. It captures the essence of individual models and describes the overall behavior of P2P Web retrieval systems. Such a framework characterizes notions like ranking, ranking aggregation and combination, etc. Algebraic properties of them are explored and guidelines for ranking computation based on those properties are proposed. Application scenarios are then given to show how different search engines and their algorithms can be formalized within this common framework. As two representative examples, heuristic and model-guided distributed ranking compositions are elaborated to demonstrate the power and potentials of this common framework. Results of my research work can be used in modern search companies to build a truly distributed search engine system; they also form a solid base for future research on topics of distributed ranking computation for P2P Web retrieval, including specific layered decentralized algorithms, and problems within the general common framework.

EPFL2006

Quantum-based approach for laboratory risk assessment and decision-making

Ruoxing Liao

2022

Distributed link-based ranking in P2P Web retrieval

Jie Wu

EPFL2006

Quantum-based approach for laboratory risk assessment and decision-making

Distributed link-based ranking in P2P Web retrieval

A Framework for Decentralized Ranking in Web Information Retrieval

Quantum-based approach for laboratory risk assessment and decision-making

Distributed link-based ranking in P2P Web retrieval

A Framework for Decentralized Ranking in Web Information Retrieval