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The purpose of this paper is to promote the efficiency of RGB-depth (RGB-D)-based object recognition in robot vision and find discriminative binary representations for RGB-D based objects.

To promote the efficiency of RGB-D-based object recognition in robot vision, this paper applies hashing methods to RGB-D-based object recognition by utilizing the approximate nearest neighbors (ANN) to vote for the final result. To improve the object recognition accuracy in robot vision, an “Encoding+Selection” binary representation generation pattern is proposed. “Encoding+Selection” pattern can generate more discriminative binary representations for RGB-D-based objects. Moreover, label information is utilized to enhance the discrimination of each bit, which guarantees that the most discriminative bits can be selected.

The experiment results validate that the ANN-based voting recognition method is more efficient and effective compared to traditional recognition method in RGB-D-based object recognition for robot vision. Moreover, the effectiveness of the proposed bit selection method is also validated to be effective.

Hashing learning is applied to RGB-D-based object recognition, which significantly promotes the recognition efficiency for robot vision while maintaining high recognition accuracy. Besides, the “Encoding+Selection” pattern is utilized in the process of binary encoding, which effectively enhances the discrimination of binary representations for objects.

Improving the diversity of recommendation information has become one of the latest research hotspots to solve information cocoons. Aiming to achieve both high accuracy and diversity of recommender system, a hybrid method has been proposed in this paper. This study aims to discuss the aforementioned method.

This paper integrates latent Dirichlet allocation (LDA) model and locality-sensitive hashing (LSH) algorithm to design topic recommendation system. To measure the effectiveness of the method, this paper builds three-level categories of journal paper abstracts on the Web of Science platform as experimental data.

(1) The results illustrate that the diversity of recommended items has been significantly enhanced by leveraging hashing function to overcome information cocoons. (2) Integrating topic model and hashing algorithm, the diversity of recommender systems could be achieved without losing the accuracy of recommender systems in a certain degree of refined topic levels.

The hybrid recommendation algorithm developed in this paper can overcome the dilemma of high accuracy and low diversity. The method could ameliorate the recommendation in business and service industries to address the problems of information overload and information cocoons.

This paper aims to describe an effort to provide for a robust and secure software development paradigm intended to support DevSecOps in a naval aviation enterprise (NAE) software support activity (SSA), with said paradigm supporting strong traceability and provability concerning the SSA’s output product, known as an operational flight program (OFP). Through a secure development environment (SDE), each critical software development function performed on said OFP during its development has a corresponding record represented on a blockchain.

An SDE is implemented as a virtual machine or container incorporating software development tools that are modified to support blockchain transactions. Each critical software development function, e.g. editing, compiling, linking, generates a blockchain transaction message with associated information embedded in the output of a said function that, together, can be used to prove integrity and support traceability. An attestation process is used to provide proof that the toolchain containing SDE is not subject to unauthorized modification at the time said critical function is performed.

Blockchain methods are shown to be a viable approach for supporting exhaustive traceability and strong provability of development system integrity for mission-critical software produced by an NAE SSA for NAE embedded systems software.

A blockchain-based authentication approach that could be implemented at the OFP point-of-load would provide for fine-grain authentication of all OFP software components, with each component or module having its own proof-of-integrity (including the integrity of the used development tools) over its entire development history.

Many SSAs have established control procedures for development such as check-out/check-in. This does not prove the SSA output software is secure. For one thing, a build system does not necessarily enforce procedures in a way that is determinable from the output. Furthermore, the SSA toolchain itself could be attacked. The approach described in this paper enforces security policy and embeds information into the output of every development function that can be cross-referenced to blockchain transaction records for provability and traceability that only trusted tools, free from unauthorized modifications, are used in software development. A key original concept of this approach is that it treats assigned developer time as a transferable digital currency.

The purpose of this study was to develop and test SCoP. Users find comparing long meaningless strings of alphanumeric characters difficult. While visual hashes – where users compare images rather than strings – have been proposed as an alternative, people are unable to sufficiently distinguish more than 30 bits, which does not provide adequate security against collision attacks. Our goal is to improve the situation.

A visual hash scheme was developed using shapes, colours, patterns and position parameters. It was evaluated in a series of pilot user studies and improved iteratively, leading to SCoP, which encodes 60 distinguishable bits. We tested SCoP further in two follow-up studies, simulating verifying in remote electronic voting and https certificate validation.

Participants attained an average accuracy rate of 97 per cent with SCoP when comparing two visual hash images, one placed above the other. From the follow-up studies, SCoP was seen to be more promising for the https certificate validation use case, with direct image comparison, while a low average accuracy rate in simulating verifiability in remote electronic voting limits its applicability in an image-recall use case.

Participants achieved high accuracy rates in unrealistic situations, where the images appeared on the screen at the same time and in the same size. Studies in more realistic situations are therefore necessary.

We identify a visual hash scheme encoding a higher number of distinguishable bits than previously reported in literature, and extend the testing to realistic scenarios.

The purpose of this paper is to examine the blockchain as a trusted computing platform. Understanding the strengths and limitations of this platform is essential to execute large-scale real-world applications in blockchains.

This paper proposes several modifications to conventional blockchain networks to improve the scale and scope of applications.

Simple modifications to cryptographic protocols for constructing blockchain ledgers, and digital signatures for authentication of transactions, are sufficient to realize a scalable blockchain platform.

The original contributions of this paper are concrete steps to overcome limitations of current blockchain networks.

The use of variety generation techniques in the production of author‐title search codes for files of monograph records is compared with methods based on division hashing. The latter perform better, and evidence is presented to suggest that the reason for this is the lack of statistical independence between the assignments of variety generation symbols to different parts of a field in the record.

One challenge for tourism recommendation systems (TRSs) is the long-tail phenomenon of ratings or popularity among tourist products. This paper aims to improve the diversity and efficiency of TRSs utilizing the power-law distribution of long-tail data.

Using Sina Weibo check-in data for example, this paper demonstrates that the long-tail phenomenon exists in user travel behaviors and fits the long-tail travel data with power-law distribution. To solve data sparsity in the long-tail part and increase recommendation diversity of TRSs, the paper proposes a collaborative filtering (CF) recommendation algorithm combining with power-law distribution. Furthermore, by combining power-law distribution with locality sensitive hashing (LSH), the paper optimizes user similarity calculation to improve the calculation efficiency of TRSs.

The comparison experiments show that the proposed algorithm greatly improves the recommendation diversity and calculation efficiency while maintaining high precision and recall of recommendation, providing basis for further dynamic recommendation.

TRSs provide a better solution to the problem of information overload in the tourism field. However, based on the historical travel data over the whole population, most current TRSs tend to recommend hot and similar spots to users, lacking in diversity and failing to provide personalized recommendations. Meanwhile, the large high-dimensional sparse data in online social networks (OSNs) brings huge computational cost when calculating user similarity with traditional CF algorithms. In this paper, by integrating the power-law distribution of travel data and tourism recommendation technology, the authors’ work solves the problem existing in traditional TRSs that recommendation results are overly narrow and lack in serendipity, and provides users with a wider range of choices and hence improves user experience in TRSs. Meanwhile, utilizing locality sensitive hash functions, the authors’ work hashes users from high-dimensional vectors to one-dimensional integers and maps similar users into the same buckets, which realizes fast nearest neighbors search in high-dimensional space and solves the extreme sparsity problem of high dimensional travel data. Furthermore, applying the hashing results to user similarity calculation, the paper greatly reduces computational complexity and improves calculation efficiency of TRSs, which reduces the system load and enables TRSs to provide effective and timely recommendations for users.

Cryptoassets have recently attracted the attention of national and international financial regulators. Since the mid-2010s blockchains have increasingly been adapted to automate and replace many aspects of financial intermediation, and by 2015 Ethereum had created the smart contract language that underpins the digitization of real assets as asset-backed tokens (ABTs). Those were initially issued by FinTech companies, but more recently banks active on international capital and financial markets, and even central banks, for example, the Bank of Thailand, have developed their own digital platforms and blockchains. A wide variety of real and financial assets underpins ABTs, viz., real-estate, art, corporate and sovereign bonds, and equity. Consequently, owing to the significant market capitalization of cryptocurrencies, the Basel Committee on Banking Supervision (BCBS) published two consultative papers delineating its approach on cryptoasset regulation. In this study, the authors analyze the mechanics of ABTs and their potential risks, relying on case studies of recent issuance of tokens in equity, real-estate, and debt markets, to highlight their main characteristics. The authors also investigate the consequences of the increasingly oligopolistic structure of blockchain mining pools and Bitcoin exchanges for the integrity and security of unregulated distributed ledgers. Finally, the authors analyze the BCBS’ regulatory proposals, and discuss the reaction of international financial institutions and cryptocurrency interest groups. The main findings are, firstly, that most ABTs are akin to asset-backed securities. Secondly, nearly all ABTs are “off-chain/on-chain,” that is, the underlying is a traditional asset that exists off-chain and is subsequently digitized. The main exception is the World Bank’s bond-i that is genuinely native to the blockchain created by the Commonwealth Bank of Australia, and has no existence outside it. Thirdly, all ABTs are issued on permissioned blockchains, where anti-money laundering/anti-terrorist funding and know-your-customer regulations are enforced. From a prudential regulatory perspective, ABTs do not appear to pose serious systemic risks to international financial markets. This may account for the often negative reactions of banks, banking associations, and cryptocurrency interest groups to the BCBS’ 2021 proposals for risk-weighted capital provisions for cryptoassets, which are viewed as excessive. Finally, we found that issuance of ABTS and other smart contracts on permissionless blockchains such as Bitcoin and Ethereum could potentially generate financial instability. A precedent involving Ethereum and The DAO in 2016 shows that (i) there is a significant accountability gap in permissionless blockchains, and (ii) the core developers of blockchains and smart contract technology, and Bitcoin mining pools, exercise an unexpectedly high- and completely unregulated-amount of power in what is supposedly a decentralized network.