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This paper aims to explore the problem of power imbalance within decentralized autonomous organizations (DAOs) and propose potential solutions that could contribute to enhancing the democratic nature of DAOs.

In this paper, the authors apply a qualitative methodology. Using a thematic coding analysis, the authors process data collected from interviews with 11 experts.

Multiple factors contribute to the perceived lack of democracy within DAOs, such as token concentration and effective stakeholder communication. Next, quadratic voting has the potential to enhance democracy within DAOs, but this mechanism must be implemented mindfully. Finally, the results were nuanced when it comes to the effectiveness of liquid democracy in DAOs to enhance voter participation and representation.

To the best of the authors’ knowledge, this paper is one of the first research contributions to propose recommendations to address the power imbalance within DAOs and to contribute to the advancement of decentralized decision-making structures.

This study aims to examine the role of institutional investors (using proxy voting and voice) in influencing the decisions and governance landscape of their investee firms.

The authors use exploratory research design due to the underdevelopment of the problem phenomena, especially in the context of emerging economies. Using asset management companies (AMC) as a proxy for institutional investors, the authors use a multiple case study design. This design was relevant in the setting as it assured triangulation by studying the same phenomenon across firms with distinct characteristics. The authors sourced the data for the multiple cases from primary sources (such as semi-structured interviews) and secondary sources (such as official Webpages and social media pages of AMC and examination of archival documents). Finally, the authors used qualitative content analysis to analyse the data.

The findings suggest that shareholder activism by institutional investors has grown in India over the period, particularly in matters related to corporate governance, related party transactions, remuneration and compensation. These AMC in India use proxy voting services for advising on voting resolutions in their investee companies. However, voting by AMC does not generally affect resolution results. This is particularly true in the presence of a high concentration of promoter holdings in investee companies.

The study is a novel attempt in an emerging market context to explore the role of institutional investors in influencing firm decisions and improving the governance landscape of the company using proxy voting and voice. This is especially important as the institutional framework in emerging markets is not as strong as in developed markets.

The individual machine learning methods used for fault detection and classification have accuracy performance at a certain level. A combined learning model composed of different base classifiers rather than an individual machine learning model is introduced to ensure diversity. In this way, this study aims to improve the generalization capability of fault detection and classification scheme.

This study presents a probabilistic weighted voting model (PWVM) with multiple learning models for fault detection and classification. The working principle of this study’s proposed model relies on weight selection and per-class possibilities corresponding to predictions of base classifiers. Moreover, it can improve the power of the prediction model and cope with imbalanced class distribution through validation metrics and F-score.

The performance of the proposed PWVM was better than the performance of the individual machine learning methods. Besides, the proposed voting model’s performance was compared with different voting mechanisms involving weighted and unweighted voting models. It can be seen from the results that the presented model is superior to voting mechanisms. The performance results revealed PWVM has a powerful predictive model even in noisy conditions. This study determines the optimal model from among voting models with the prioritization method on data sets partitioned different ratios. The obtained results with statistical analysis verified the validity of the proposed model. Besides, the comparative results from different benchmark data sets verified the effectiveness and robustness of this study’s proposed model.

The contribution of this study is that PWVM is an ensemble model with outstanding generalization capability. To the best of the authors’ knowledge, no study has been performed using a PWVM composed of multiple classifiers to detect no-faulted/faulted cases and classify faulted phases.

Does social-isolation deflate observed preferences for public goods? Using a voting referendum elicitation mechanism, List et al. (2004) document a 30% reduction in affirmative voting when votes are privately, rather than publicly, cast for a public good. We replicate this work and then add to the exercise by examining the role that group size plays in generating social-pressure bias – an extension we motivate with a structural model. Having replicated to the extent possible the List et al.’s consequential private and public treatments, we find that when group size is large (N = 60), social isolation reduces affirmative votes by roughly 30%, a result that does not carry over to the small-group (N = 30) session.

In observational data, access to information is associated with lower levels of corruption. This chapter reviews a small but growing body of work that uses field experiments to explore the mechanisms behind this relationship. We present a typology for understanding this research based on the type of corruption being addressed (political vs. bureaucratic), the mechanism for accountability (retrospective vs. prospective), and the nature of the information provided (factual vs. prescriptive). We describe some of the tradeoffs involved in design decisions for such experiments and suggest directions for future research.

As the core technology of blockchain, various consensus mechanisms have emerged to satisfy the demands of different application scenarios. Since determining the security, scalability and other related performance of the blockchain, how to reach consensus efficiently of consensus mechanism is a critical issue in the blockchain.

The paper opted for a research overview on the blockchain consensus mechanism, including the consensus mechanisms' consensus progress, classification and comparison, which are complemented by documentary analysis.

This survey analyzes solutions for the improvement of consensus mechanisms in blockchain that have been proposed during the last few years and suggests future research directions around consensus mechanisms. First, the authors outline the consensus processes, the advantages and disadvantages of the mainstream consensus mechanisms. Additionally, the consensus mechanisms are subdivided into four types according to their characteristics. Then, the consensus mechanisms are compared and analyzed based on four evaluation criteria. Finally, the authors summarize the representative progress of consensus mechanisms and provide some suggestions on the design of consensus mechanisms to make further advances in this field.

This paper summarizes the future research development of the consensus mechanisms.

The purpose of this study is to use role expectation theory to identify potential determinants of user voting avoidance on mobile social media.

Data were collected through a survey of 602 WeChat users, and the proposed model was analysed using structural equation modelling.

Results indicate that user voting avoidance was positively influenced by unfair competition, perceived inauthenticity, perceived information insecurity, over-consumption of renqing (a unique Chinese human relation) and organisation placement in the context of mobile social media.

This study illustrates mobile user voting avoidance from the perspective of role expectation theory and clarifies the importance of avoidance in current voting research.

The chapter is aimed at comprehensive discovery of the basic legal tools to improve the quality of corporate governance from the standpoint of efficient revealing, preventing and shooting negative implications of internal corporate disputes. The subject of the study, in particular, is regulatory and non-regulatory enactments in this area. Within the study we employ basic approaches that allow to reveal the conditions ensuring efficient corporate governance, i.e., dialectical, historical, systemic, formal-logic, and comparative-law ones. Therewith, the main focus is on the application of system theory developments in terms of the methodological techniques to describe any system object.

Regarding the disputes between participants of the corporation, we substantiate the need of creating conditions for the highest account of opinions of all the participants in the development of collective will, as well as for restraint of major participants from potential abuses (through accumulative voting mechanism, implementing “two keys” principle, etc.). Special attention is paid to the issues of procedure and content of interest-collision transactions made by economic societies.

The authors point out definite guidelines to improve legislation in anti-conflict aspect (forms of holding general meetings of participants, exclusive and non-exclusive competence of governing bodies, the content of acts coordinating interested-party transactions, etc.).

The problem of image retrieval and image description exists in various fields. In this paper, a model of content-based image retrieval and image content extraction based on the KD-Tree structure was proposed.

A Random Forest structure was built to classify the objects on each image on the basis of the balanced multibranch KD-Tree structure. From that purpose, a KD-Tree structure was generated by the Random Forest to retrieve a set of similar images for an input image. A KD-Tree structure is applied to determine a relationship word at leaves to extract the relationship between objects on an input image. An input image content is described based on class names and relationships between objects.

A model of image retrieval and image content extraction was proposed based on the proposed theoretical basis; simultaneously, the experiment was built on multi-object image datasets including Microsoft COCO and Flickr with an average image retrieval precision of 0.9028 and 0.9163, respectively. The experimental results were compared with those of other works on the same image dataset to demonstrate the effectiveness of the proposed method.

A balanced multibranch KD-Tree structure was built to apply to relationship classification on the basis of the original KD-Tree structure. Then, KD-Tree Random Forest was built to improve the classifier performance and retrieve a set of similar images for an input image. Concurrently, the image content was described in the process of combining class names and relationships between objects.

This paper aims to apply blockchains (BCs) for trade clearing and settlement in a realistic clearinghouse. The purpose is to demonstrate the feasibility and scalability of this approach.

The study uses account BCs and trading BCs as building blocks for trade clearing and settlement. Careful design is made to ensure that this approach is feasible and scalable.

A design has been proposed that can process hundreds of thousands of trades for a clearinghouse and it addresses performance, privacy and scalability of realistic trade clearing and settlement. The design has been implemented and experimented in a clearinghouse for over two months and processes over 3B real transactions from an exchange. The first month was to experiment with the system with historical data, the second month was to experiment with real-time data during market trading hours. The system performed as designed and intended.

This is the first large research paper that applied BCs for clearing in the world. The authors applied the system to a clearinghouse and processed over 3 billion transactions, equivalent to 13 years of London Stock Exchange transaction volume, demonstrating that BCs can handle a large number of transactions.

The design can be duplicated to many clearinghouses in the world, and this also paves the way BCs can be used in large financial institutions.

An implication is that other trading firms, clearinghouses and banks can apply the same technology for trade clearing, ushering the way BCs can be used in institutions. As clearing is a core function in business transactions, this has significant implications. The design can be discussed and improved in various communities.

As this is the first application of BCs to large clearinghouses that uses unique BC designs. This has significant value. Many studies have been performed but few have been reported in the scientific community. The system has been implemented, experimented and demonstrated in public for months.