Reviewerly/exHarmony huggingface dataset:
A Directly Readable Halftone Multifunctional Color QR Code

Color quick response (QR) code is an important direction for the future development of QR code, which has become a research hotspot due to the additional functional characteristics of its colors as the wide application of QR code technology.The existing color QR code has solved the problem of information storage capacity, but it requires an enormous hardware and software support system, making how to achieve its direct readability an urgent issue.This paper proposes a novel color QR code that combines multiple types of different identification information.This code combines multiplexing and color-coding technology to present the publicly encoded information (such as advertisements, public query information) as plain code, and traceability, blockchain, anticounterfeiting authentication and other information concealed in the form of hidden code.We elaborate the basic principle of this code, construct its mathematical model and supply a set of algorithm design processes, which breakthrough key technology of halftone printout.The experimental results show that the proposed color quick response code realizes the multi-code integration and can be read directly without special scanning equipment, which has unique advantages in the field of printing anticounterfeiting labels.
NICHE: A Curated Dataset of Engineered Machine Learning Projects in Python

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such a high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on the evidence of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. This dataset can help researchers understand the practices that are adopted in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.
Orion: A Centralized Blockchain Database with Multi-Party Data Access Control

Blockchain databases were designed to improve trust in centralized ecosystems, which dominate the market today, by introducing tamper-evidence features on top of a classical database. Compared to decentralized ledger technologies, blockchain databases are easier to use, and they can significantly reduce the operational and development costs. However, the existing blockchain databases do not equip multiple parties with tools to efficiently control common data written to the ledger. This paper describes Orion, a new open source blockchain database that introduces multi-sig and proof capabilities with extensive key-level access control, which enable parties to mutually control and validate a value written to the database. These unique capabilities, together with additional blockchain properties, provide users with features such as tamper-evidence, provenance, data lineage, authenticity, and non-repudiation, while using a standard data model and transactional APIs. We found our technology to be extremely useful in improving the integrity of a system and reducing mistakes, disputes, and fraud.
Structured Pools for Tokenized Carbon Credits

Tokenized carbon credits are of growing relevance to voluntary carbon markets, but because tokenized carbon credits are typically NFTs, they suffer from low liquidity and high price volatility on-chain. Current solutions pool carbon credits to enable greater liquidity, but are incomplete. We present a novel pooling mechanism which we call a structured pool, which addresses issues with current pooling techniques.
A First Analytics Approach to Cardano

Many variants of (delegated) Proof-of-Stake blockchains have been introduced since 2016, among which Cardano, Algorand, and Tezos have been more widely adopted. Each of these blockchains has attempted to address the scalability and consumption issues of Proof-of-Work (PoW) blockchains from different angles. However, the economic incentives of these blockchains have raised the concern about the decentralisation level of key players, such as validators, and stakeholders, in those “expected to be decentralised” networks. In this work, we conduct an analysis of Cardano's activity and wealth concentration. For the first time, we perform a clustering of the addresses that belong to the same wallet based on existing heuristics for UTXO-like blockchains. Moreover, an analysis of reward distribution on Cardano is presented. The analysis results of this work disclose objective insights into the Cardano blockchain, including the rewards distribution, stake balance distribution, and wealth concentration.
FLOATING: Federated Learning for Optimized Automated Trajectory Information StoriNG on Blockchain

Trajectory data from mobile, micro-mobility devices (e-scooter, e-bikes, etc) and vehicles need validation regarding its trustworthiness for utility in different applications. Sharing of false trajectories from compromised devices can lead to potentially fatal consequences for safety-related applications. There is no scalable method to assess the truthfulness of trajectory data in real-time, therefore, this paper proposes FLOATING, leveraging federated reinforcement learning to automate trajectory validation on a private-by-design blockchain. FLOATING employs a three-tier consensus process for nodes in each others vicinity to endorse trajectories in real-time. We evaluate FLOATING using NS-3 and it shows to achieve lower delays and network overhead for a network size of up to 50 nodes participating in the consensus, while reducing network resource utilization by 10 times.
Allowing Blockchain Loans with Low Collateral

Collateral is an item of value serving as security for the repayment of a loan. In blockchain-based loans, cryptocurrencies serve as the collateral. The high volatility of cryptocurrencies implies a serious barrier of entry with a common practice that collateral values equal multiple times the value of the loan. As assets serving as collateral are locked, this requirement prevents many candidates from obtaining loans. In this paper, we aim to make loans more accessible by offering loans with lower collateral, while keeping the risk for lenders bound. We use a credit score based on data recovered from the blockchain to predict how likely someone is to repay a loan. Our protocol does not risk the initial amount granted by liquidity providers, but only risks part of the interest yield gained by the protocol in the past.
Alleviating Crypto Gas War in NFT Launching

Non-Fungible Tokens (NFT) is a unique digital token based on blockchain technology, which is used as a means to prove ownership of assets in various areas. With the huge popularity of NFT, the launch of new NFTs attracted many people to mint NFTs. However, because Ethereum has low processing speed and then cannot accommodate the explosive demands, it causes Crypto Gas War, which increases the overall transaction fee and wastes unnecessary gas. In this work, we propose a Raffle-based N FT launch to solve t he C rypto Gas War. We demonstrated that our proposed NFT launch solves this problem but other existing solutions do not solve the problem. Moreover, our NFT smart contract is gas-efficient. Our proposed method reduces the gas usage by 15.5% or more compared to general method, and the efficiency is improved a ccording to increasing the total number of NFTs.
Graph-Centric Crypto Price Prediction

In this paper we propose, for the first time in the literature, a graph-centric approach for crypto price prediction through the use of Graph Neural Networks, which can naturally be applied over transaction graphs and thus exploit the connectivity and structural features of Blockchains' transaction network. We apply our approach to the prediction of the most popular cryptocurrency currently on the market, Bitcoin. We demonstrate the effectiveness of our methods for short and longer term predictions considering several variants of the Bitcoin transaction graphs, additional external information and various basic architectures, achieving results with mean absolute percentage error as low as 1.069 %. Compared to the state-of-the-art, our method exhibits more useful results, in terms of both accuracy and predictive power.
CLEAN++: Code Smells Extraction for C++

The extraction of features is an essential step in the process of mining software repositories. An important feature that has been actively studied in the field of mining software repositories is bad code smells. Bad code smells are patterns in the source code that indicate an underlying issue in the design and implementation of the software. Several tools have been proposed to extract code smells. However, currently, there are no tools that extract a significant number of code smells from software written in C++. Therefore, we propose CLEAN++ (Code smeLls ExtrActioN for C++) [1]. It is an extension of a robust static code analysis tool that implements 35 code smells. To evaluate CLEAN++, we ran it over 44 open-source projects and wrote test cases to validate each code smell. Also, we converted the test cases to Java and used two Java tools to validate the effectiveness of our tool. In the end, we confirmed that the CLEAN++ is successful at detecting code smells.The tool is available at https://github.com/Tomma94/CLEAN-Plus-Plus.
Möbius: an Atomic State Sharding Design for Account-Based Blockchains

This paper presents Mobius, the first cost-efficient state sharding design that remains consensus mechanism agnostic and guarantees atomicity for cross-shard smart contract transactions. In particular, to address the challenges posed by the growing blockchain state, Mobius enables its participants to verify all transactions while only storing a partial state. Unlike previous state sharding systems, the proposed protocol uses a novel vector commitment data structure to reduce the network bandwidth overhead via proof aggregation. Further, it utilizes a novel epoch-based multi-phase commitment technique for guaranteeing atomicity in cross-shard transactions. Experiments presented here show that Mobius reduces the disk requirement of each participant linearly with respect to the number of shards. Further, it presents a 4.7-7.3x lower network bandwidth overhead when compared to existing state-of-the-art state sharding systems. The outcomes also confirm that existing smart contracts can operate on Mobius in cross-shard scenarios without modifications.
DEEPJUDGE: A Testing Framework for Copyright Protection of Deep Learning Models

Deep learning (DL) models have become one of the most valuable assets in modern society, and those most complex ones require millions of dollars for the model development. As a result, unauthorized duplication or reproduction of DL models can lead to copyright infringement and cause huge economic losses to model owners. In this work, we present Deepjudge, a testing framework for DL copyright protection. Deep Judgequantitatively tests the similarities between two DL models: a victim model and a suspect model. It leverages a diverse set of testing metrics and efficient test case generation algorithms to produce a chain of supporting evidence to help determine whether a suspect model is a copy of the victim model. Our experiments confirm the effectiveness of Deepjudge under typical model copyright infringement scenarios. The tool has been made publicly available at https://github.com/Testing4AI/DeepJudge. A demo video can be found at https://www.youtube.com/watch?v=LhNeo615YOE.
Towards machine learning guided by best practices

Nowadays, machine learning (ML) is being used in software systems with multiple application fields, from medicine to software engineering (SE). On the one hand, the popularity of ML in the industry can be seen in the statistics showing its growth and adoption. On the other hand, its popularity can also be seen in research, particularly in SE, where multiple studies related to the use of Machine Learning in Software Engineering have been published in conferences and journals. At the same time, researchers and practitioners have shown that machine learning has some particular challenges and pitfalls. In particular, research has shown that ML-enabled systems have a different development process than traditional software, which also describes some of the challenges of ML applications. In order to mitigate some of the identified challenges and pitfalls, white and gray literature has proposed a set of recommendations based on their own experiences and focused on their domain (e.g., biomechanics), but for the best of our knowledge, there is no guideline focused on the SE community. This thesis aims to reduce the gap of not having clear guidelines in the SE community by using possible sources of practices such as question-and- answer communities and also previous research studies. As a result, we will present a set of practices with an SE perspective, for researchers and practitioners, including a tool for searching them.
Are We Speeding Up or Slowing Down? On Temporal Aspects of Code Velocity

This paper investigates how the duration of various code review periods changes over a projects’ lifetime. We study four open-source software (OSS) projects: Blender, FreeBSD, LLVM, and Mozilla. We mine and analyze the characteristics of 283,235 code reviews that cover, on average, seven years’ worth of development. Our main conclusion is that neither the passage of time or the project’s size impact code velocity. We find that (a) the duration of various code review periods (time-to-first-response, time-to-accept, and time-to-merge) for FreeBSD, LLVM, and Mozilla either becomes shorter or stays the same; no directional trend is present for Blender, (b) an increase in the size of the code bases (annually 3–17%) does not accompany a decrease in code velocity, and (c) for FreeBSD, LLVM, and Mozilla, the 30-day moving median stays in a fixed range for time-to-merge. These findings do not change with variabilities in code churn metrics, such as the number of commits or distinct authors of code changes.
PENTACET data - 23 Million Contextual Code Comments and 250,000 SATD comments

Most Self-Admitted Technical Debt (SATD) research utilizes explicit SATD features such as ‘TODO’ and ‘FIXME’ for SATD detection. A closer look reveals several SATD research uses simple SATD (‘Easy to Find’) code comments without contextual data (preceding and succeeding source code context). This work addresses this gap through PENTACET (or 5C dataset) data. PENTACET is a large Curated Contextual Code Comments per Contributor and the most extensive SATD data. We mine 9,096 Open Source Software Java projects totaling over 400 million LOC. The outcome is a dataset with 23 million code comments, preceding and succeeding source code context for each comment, and more than 250,000 SATD comments, including both ‘Easy to Find’ and ‘Hard to Find’ SATD. We believe PENTACET data will further SATD research using Artificial Intelligence techniques.
Service Applicable Blockchain-based Self-Sovereign Identity Management System

Managing identity is a crucial issue, and with the advent of digital identity, Self-Sovereign Identity (SSI) has been highlighted. SSI allows the owner to have sovereignty over their identity information. Currently, blockchain technology is widely used to implement SSI management system. Issuing a verifiable credential (VC) based on blockchain has two advantages: it ensures the VC has not been tampered with and makes the VC trustworthy by sharing the same ledger among untrusted parties. This paper presents a SSI management system which was designed and implemented for police or firefighters who still manage identity information centrally. Furthermore, given the inherent risks associated with these occupations, it is imperative to have a secure healthcare system to meet their specific needs. We propose a system that links these healthcare systems, allowing owners of identity information to monitor how their identity information has been used. The usage history of the system is stored in a permissioned blockchain, specifically Hyperledger Fabric.
Methodology and Guidelines for Evaluating Multi-Objective Search-Based Software Engineering

Search-Based Software Engineering (SBSE) has been becoming an increasingly important research paradigm for automating and solving different software engineering tasks. When the considered tasks have more than one objective/criterion to be optimized, they are called multi-objective ones. In such a scenario, the outcome is typically a set of incomparable solutions (i.e., being Pareto nondominated to each other), and then a common question faced by many SBSE practitioners is: how to evaluate the obtained sets by using the right methods and indicators in the SBSE context? In this comprehensive technical brief, we seek to provide a systematic methodology and guidelines for answering this question. We start off by discussing why we need formal evaluation methods/indicators for multi-objective optimization problems in general, and the result of a survey on how they have been dominantly used in SBSE. This is then followed by a detailed introduction of representative evaluation methods and quality indicators used in SBSE, including their behaviors and preferences. In the meantime, we demonstrate the patterns and examples of potentially misleading usages/choices of evaluation methods and quality indicators from the SBSE community, highlighting their consequences. Afterward, we present a systematic methodology that can guide the selection and use of evaluation methods and quality indicators for a given SBSE problem in general, together with pointers that we hope to spark dialogues about some future directions on this important research topic for SBSE. Lastly, we showcase several real-world multi-objective SBSE case studies, in which we demonstrate the consequences of incorrect usage and exemplify the implementation of the guidance provided.
Personalized action suggestions in low-code automation platforms

Automation platforms aim to automate repetitive tasks using workflows, which start with a trigger and then perform a series of actions. However, with many possible actions, the user has to search for the desired action at each step, which hinders the speed of flow development. We propose a personalized transformer model that recommends the next item at each step. This personalization is learned end-to-end from user statistics that are available at inference time. We evaluated our model on workflows from Power Automate users and show that personalization improves top-1 accuracy by 22%. For new users, our model performs similar to a model trained without personalization.
Complementing Secure Code Review with Automated Program Analysis

Code review is an important activity in software en-gineering process to reduce software defects before the production phase. It is crucial that software defects are identified as soon as they are introduced because their impact can be amplified if they are discovered in the later stages. However, previous studies have observed that vulnerability, a software weakness that could be exploited by an attacker, can slip through the code review process because of the limited resources and security awareness of reviewers. Approaches such as automated program analysis have been recommended to assist this problem. Yet, it is unclear about the capability of automated program analysis to augment human reviewers on the security aspects. This research project aims to investigate to what extent, and how, can different automated program analysis approaches complement human reviewers in the code review process.
ML on Chain: The Case and Taxonomy of Machine Learning on Blockchain

Blockchain has recently amassed a lot of interest from researchers and practitioners. This is due to its ability to manage data in a decentralized, transparent and accountable manner. However, blockchain applications are limited in terms of compute and cost effectiveness. These limits led to smart contracts—programs that are processed by the blockchain network—being relatively simple, not taking advantage of complex computation methods. In this paper, we shed light on the design space of using machine learning in blockchain applications (we call this ML on Chain for short). Although a lot of work discusses the intersection of blockchain and ML, there is little clarity in mapping, understanding, and evaluating the various approaches in this space. This paper contributes to this new area by the following: (1) we introduce a taxonomy of ML on Chain. This taxonomy considers existing and future potential methods in this area and groups them based on their design characteristics. We consider in the taxonomy five off-chain approaches and a baseline on-chain approach. (2) we perform an extensive experimental evaluation of ML on Chain methods. We compare the different 5 groups of solutions across different settings and three ML model types: logistic regression, k-nearest neighbors and neural networks. (3) Using the taxonomy and experimental study, we provide insights about the current state and challenges of the ML on Chain space; we use this newfound understanding to discuss potential future approaches and provide suggestions to address ML on Chain challenges.
Unveiling the Relationship Between Continuous Integration and Code Coverage

Continuous integration (CI) is a software engineering practice that advocates the frequent integration of software through an automated build process. Existing research has explored the benefits of CI, such as detecting errors earlier in the software life-cycle. Although CI heavily focuses on automated tests, it is still unclear whether CI is associated with better code coverage, which could be a major benefit of using CI. To investigate whether CI is associated with an improvement in code coverage, our work compares 30 projects that adopted CI (CI projects) and 30 projects that have never adopted CI (NOCI projects). In total, we studied 1,440 versions from different projects to analyze trends in code coverage related to CI. While evaluating trends of code coverage within CI and NOCI projects, we observe more projects with rising trends of code coverage in CI projects (50%) than NOCI projects (10%). Moreover, the maintaining trends are different, as CI projects tend to stabilize at a higher code coverage rate than NOCI projects. Investigating the CI projects alone, the statistical evidences indicate that the adoption of CI is associated with the increase in code coverage. The findings of this study, therefore, reveal a positive association between CI and a higher code coverage rate.