Reviewerly/exHarmony huggingface dataset:
A Blockchain-based Data Sharing Marketplace with a Federated Learning Use Case

Due to the sharp growth of employing mobile devices and IoT (Internet of Things) sensors in daily life, tremendous generated or collected data become one of the most valuable assets for not only users but also numerous applications, which provide various services using user data. However, a large portion of such data is possessed by only a few giant companies in a centralized manner. This incurs the concerns of how user data are harnessed and used and who can use such data because of many cases of privacy violence and data leakage. Therefore, in this paper, we propose a decentralized data sharing marketplace using Ethereum to enable users to share their data in a privacy-preserving and self-governing manner. Users can only share parts of the data from their devices they want to share in the marketplace and gain rewards from the bidding of buyers anonymously. Furthermore, a federated learning use case is demonstrated as a privacy-enhanced application of the proposed marketplace to encourage users to share processed data to avoid raw data leakage.
NFT SMASH: Game to Test Your NFT Rarity Sense

In the demo, we present NFT SMASH-a game to test your NFT rarity sense. A rarity meter aims to be an immanent value estimator for NFTs: the rarer the NFT, the more expensive it is now compared to other NFTs within the same collection. While working with rarity meters, we designed and developed a game to examine how intuitive are their results. The BAYC collection data mining, rarity computation with the Kramer algorithm, and a web game application are essential parts of the demo design. The game is a five-round binary choice of picture followed by the game run summary.
An Empirical Study on the Performance of Individual Issue Label Prediction

In GitHub, open-source software (OSS) developers label issue reports. As issue labeling is a labor-intensive manual task, automatic approaches have developed to label issue reports. However, those approaches have shown limited performance. Therefore, it is necessary to analyze the performance of predicting labels for an issue report. Understanding labels with high performance and those with low performance can help improve the performance of automatic issue labeling tasks. In this paper, we investigate the performance of individual label prediction. Our investigation uncovers labels with high performance and those with low performance. Our results can help researchers to understand the different characteristics of labels and help developers to develop a unified approach that combines several effective approaches for different kinds of issues.
A Smart Contract-based Decentralized Marketplace System to Promote Reviewer Anonymity

In recent years, online marketplaces have seen a large increase in business. Managing and making sellers' reputations available to buyers is vital for the success of these markets since buyers may be dealing with unknown online sellers. Typically, prospective buyers assess sellers' performance by looking at past buyers' reviews, prior to making a purchase. In current online marketplaces, buyers' reviews are associated with their identities, raising privacy issues. Several decentralized solutions have been proposed using blockchain technologies to solve this problem. However, some of these systems are not entirely decentralized or have some weaknesses such as requiring a trusted third party. In this paper, we propose a system for decentralized reviewing that trades off cost for unlinkability. Using blockchain-based smart contracts, we propose a fully decentralized marketplace to provide reviewers with a secure and trusted platform. Here, buyers can use one-time identities to provide feedback on their transactions. The system also ensures that a review can only be submitted after a transaction is completed, and that at most one review may be submitted per transaction. The system enforces these properties through blockchain technologies and smart contracts. In addition, we propose an innovative approach to encourage buyers to submit reviews. The system has been prototyped using Remix IDE.
Decentralized Machine Learning Governance

Researchers have started to recognize the necessity for a well-defined ML governance framework based on the principle of decentralization and comprehensively defining its scope of research and practice due to the growth of machine learning (ML) research and applications in the real world and the success of blockchain-based technology. In this paper, we study decentralized ML governance, which includes ML value chain management, decentralized identity for the ML community, decentralized ownership and rights management of ML assets, community-based decision-making for the ML process, decentralized ML finance, and risk management.
The Landscape of Source Code Representation Learning in AI-Driven Software Engineering Tasks

Appropriate representation of source code and its relevant properties form the backbone of Artificial Intelligence (AI)/ Machine Learning (ML) pipelines for various software engineering (SE) tasks such as code classification, bug prediction, code clone detection, and code summarization. In the literature, researchers have extensively experimented with different kinds of source code representations (syntactic, semantic, integrated, customized) and ML techniques such as pre-trained BERT models. In addition, it is common for researchers to create hand-crafted and customized source code representations for an appropriate SE task. In a 2018 survey [1], Allamanis et al. listed nearly 35 different ways of of representing source code for different SE tasks like Abstract Syntax Trees (ASTs), customized ASTs, Control Flow Graphs (CFGs), Data Flow Graphs (DFGs) and so on. The main goal of this tutorial is two-fold (i) Present an overview of the state-of-the-art of source code representations and corresponding ML pipelines with an explicit focus on the merits and demerits of each of the representations (ii) Practical challenges in infusing different code-views in the state-of-the-art ML models and future research directions.
MROS: A framework for robot self-adaptation

Self-adaptation can be used in robotics to increase system robust- ness and reliability. This work describes the Metacontrol method for self-adaptation in robotics. Particularly, it details how the MROS (Metacontrol for ROS Systems) framework implements and pack- ages Metacontrol, and it demonstrate how MROS can be applied in a navigation scenario where a mobile robot navigates in a factory floor. Video: https://www.youtube.com/watchvISe9aMskJuE
Necessary Condition for the Success of Synchronous GNSS Spoofing

A synchronous GNSS generator spoofer aims at directly taking over the tracking loops of the receiver with the lowest possible spoofing to signal ratio (SSR) without forcing it to lose lock. This paper investigates the factors that affect spoofing success and their relationships. The necessary conditions for successful spoofing are obtained by deriving the code tracking error in the presence of spoofing and analyzing the effects of SSR, spoofing synchronization errors, and receiver settings on the S-curve ambiguity and code tracking trajectory. The minimum SSRs for a successful spoofing calculated from the theoretical formulation agree with Monte Carlo simulations at digital intermediate frequency signal level within 1 dB when the spoofer pulls the code phase in the same direction as the code phase synchronization error, and the required SSRs can be much lower when pulling in the opposite direction. The maximum spoofing code phase error for a successful spoofing is tested by using TEXBAT datasets, which coincides with the theoretical results within 0.1 chip. This study reveals the mechanism of covert spoofing and can play a constructive role in the future development of spoofing and anti-spoofing methods.
HealthChain: A Blockchain-aided Federated Healthcare Management System

Privacy and integrity of medical records while preserving and sharing them within any healthcare system is of high importance. Recently, India's Ayushman Bharat Digital Mission (ABDM) aims to address various issues of the existing healthcare systems and proposes a federated healthcare system to bring all the players under a common umbrella. However, it falls short on various aspects, including transparency, auditability, and traceability of activities and information within the system. In this proposal, we aim to bridge these existing gaps and propose an extension of the ABDM proposal by incorporating a blockchain layer instrumented with additional features such as traceable consent management, auditable access logs, etc., while keeping in mind its performance and scalability. We present a proof of concept of our proposal based on Hyperledger Fabric and related backend technologies and manifest experimental results to reinforce the practicality of our proposal.
Improving Agile Planning for Reliable Software Delivery

Agile software development prioritizes the delivery of working software. However, there are challenges in the sprint planning process that could impact the reliability of sprint delivery. In this paper, we list three challenges related to the sprint planning process. We also discuss future work directions to facilitate the sprint planning process and mitigate those challenges for software teams.
INTERPRETATION OF A MUSICAL TEXT: TO THE QUESTION OF UNDERSTANDING THE AUTHOR'S INTENTION

The article deals with the phenomenon of musical text in the aspect of interpretation. The main idea of the study is the assertion that musical interpretation is the art of embodying the author's thought in sounds, reproducing the performer's own concept. Being a category of art history, this phenomenon implies not only a variety of performing schools, but also the aesthetic principles of performance, technological problems of approaches to the musical text, its understanding. The field of interpretation in the sense of performance realization has a completely unique dimension in music. However, the theoretical foundations on which the transformations of the author's text are based do not fully reveal the artistic meanings, their cognitive significance. Thus, the relevance of the issues under study is determined by the need for a theoretical study of the specifics of the interaction of the interpretive form (the author's text) and the ability to form images through the performer's sensory cognition as a product of new semantic contexts.
TRENDS OF MODERN CHORAL CREATIVITY IN UKRAINE: A VIEW IN THE 21ST CENTURY

The article reveals the current state of choral art in Ukraine, as well as analyzes the transformations characteristic of choral performance at the turn of the 20th and 21st centuries, and also affirms the fact that the meaningful activity of choirs has always demonstrated a certain systemic nature. The features of creative experiments and genre modalities are considered. The process of expansion of stage space, synthesis of arts, author's interpretations is analyzed. Also, the conclusion is made that one of the directions for further analytical research in the field of modern choral creativity is the revision of pedagogical attitudes and methodological approaches: the interaction of academic education, digital technologies, individual author's methods, and creative experiments.
Weather type clustering recognition strategy for dual-axis mode of photovoltaic power

For photovoltaic power systems, the recognition of weather type is the basis for working mode switching. The wrong choice of working mode restricts power generation efficiency. A weather type clustering recognition strategy first is proposed, which is based on solar radiation intensity, radiation intensity fluctuation value and wind speed. Then, the weather types are summarized and merged to obtain four broad weather types: sunny, cloudy, rainy and windy. The corresponding working mode is set by four broad weather types for the photovoltaic power system. Finally, the weather recognition dual-axis tracking system is compared with the current mainstream photovoltaic power system. The results show that the weather recognition dual-axis tracking system accurately distinguishes different meteorological types throughout the year. The solar energy utilization efficiency and environmental adaptability are improved for the dual-axis tracking system.
Remote Data Auditing for Cloud-Assisted WBANs with Pay-as-You-Go Business Model

As an emerging technology, cloud-assisted wireless body area networks (WBANs) provide more convenient services to users. Recently, many remote data auditing protocols have been proposed to ensure the data integrity and authenticity when data owners outsourced their data to the cloud. However, most of them cannot check data integrity periodically according to the pay-as-you-go business model. These protocols also need high tag generation computation overhead, which brings a heavy burden for data owners. Therefore, we construct a lightweight remote data auditing protocol to overcome all above drawbacks. Our work can be deployed in the public environment without secret channels. It makes use of certificate-based cryptography which gets rid of certificate management problems, key escrow problems, and secret channels. The security analysis illustrates that the proposed protocol is secure. Moreover, the performance evaluation implies that our work is available in cutting down computation and communication overheads.
Path Planning with Improved Curve Boundary Generation for Unmanned Surface Vehicle

This paper proposes a search-optimization-based path planning strategy for Unmanned Surface Vehicle (USV) is proposed by improving the boundary generation method. Compared with the existing sampling or graph theory approaches, both the accuracy and the efficiency of collision detection are considered in the proposed strategy. The path parameterization method provided in this paper can obtain a smoother continuous path within a reasonable time-consuming range, making it more compatible with speed or trajectory planning. Finally, numerical simulations are performed to verify the effectiveness of the proposed algorithm.
Annealing Quantum Computing: An Overview

This paper aims to provide an accessible overview of annealing quantum computing. We give high-level explanations of the key quantum properties that are leveraged by a quantum annealer, along with the quantum mathematical formalism involved. Additionally, we provide information about hardware and software available for running quantum annealing algorithms, and references for further reading.
Fast Falcon Signature Generation and Verification Using ARMv8 NEON Instructions

We present our speed records for Falcon signature generation and verification on ARMv8-A architecture. Our implementations are benchmarked on Apple M1 'Firestorm', Raspberry Pi 4 Cortex-A72, and Jetson AGX Xavier. Our optimized signature generation is $$2\times $$ slower, but signature verification is 3–3.9 $$\times $$ faster than the state-of-the-art CRYSTALS-Dilithium implementation on the same platforms. Faster signature verification may be particularly useful for the client side on constrained devices. Our Falcon implementation outperforms the previous work targeting Jetson AGX Xavier by the factors $$1.48\times $$ for signing in falcon512 and falcon1024, $$1.52\times $$ for verifying in falcon512, and $$1.70\times $$ for verifying in falcon1024. We achieve improvement in Falcon signature generation by supporting a larger subset of possible parameter values for FFT-related functions and applying our compressed twiddle-factor table to reduce memory usage. We also demonstrate that the recently proposed signature scheme Hawk, sharing optimized functionality with Falcon, has $$3.3\times $$ faster signature generation and 1.6–1.9 $$\times $$ slower signature verification when implemented on the same ARMv8 processors as Falcon.
Efficient Cloud Server Deployment Under Demand Uncertainty

A main challenge faced by cloud providers is to ensure that they are ready to accommodate the growing demand for compute resources. Towards that goal, providers need to deploy cloud servers agilely for uncertain future demand under many practical business constraints while avoiding unnecessarily large operational costs. In this paper, we introduce the cloud server deployment problem. One important aspect of the problem is that the infrastructure preparation work has to be planned for before server deployments can take place. Furthermore, a combination of temporal constraints (e.g., projected dock dates) has to be considered together with a variety of physical constraints (e.g., hardware compatibility and daily deployment throughput at the data centers). While the problem shares similarities with other supply chain problems, its collection of characteristics requires new solutions that explicitly account for demand uncertainty. We formulate the underlying optimization problem as a two-stage stochastic program, and develop efficient and exact Benders-type algorithms that exploit the special structure of the second stage problem while accommodating different risk measures. We test our proposed algorithms with real production traces from Microsoft Azure, and demonstrate their effectiveness in cost reductions.
Maravedí: A Secure and Practical Protocol to Trade Risk for Instantaneous Finality

The efficiency of blockchain systems is often compared to popular credit card networks with respect to the transactions per second rate. This seems to be an unfair comparison since these networks do not complete a transaction from beginning to end. Rather they buy the risk and settle it much later. Typically transactions have only two players, the payer and the payee, and the settlement of this transaction requires time since it depends on basic properties of the consensus protocol. In practice, the payee, very often, needs to wait for confirmation in order to ship the traded goods. Alternatively, the payee, or merchant, can ship it in faith that the transaction will be confirmed. Our contribution, the Maravedí Protocol, introduces a third player to minimize the risk of the payee to be left without the payment even without the consensus layer confirmation. The main idea is that the third player can work similarly to a credit card company. That is, it buys the risk from the merchant, by a small discount, and allows the third player to pay it instantaneously via a payment-channel like protocol. In parallel, the third player receives the regular payment transaction from the payer that can be settled on the chain, thus, after waiting the consensus/blockchain required time. Moreover, the on-chain transaction pays the full amount, allowing the third player to cash in the discount. Hence, on the side of the merchant, our protocol puts forth instantaneous finality in a novel way to the best of our knowledge.
Self-Repair in Russian Spoken Discourse in Psycholinguistics Aspect: Correlation Analysis and Quantitative Data

The article is aimed at revealing and describing the correlation between the number and usage of self-repairs in monologues and the psychological characteristics of the speaker. Self-repair is the correction of one’s own speech fragment which has already been produced. The usage of a particular self-repair strategy by different psychological groups of speakers can show how the speakers’ psychotype can affect their speech production. The data for the study are 24 monologues-descriptions in Russian from the corpus “Balanced Annotated Text Library”: 12 monologues of native Russian speakers and 12 monologues of foreign Russian speakers. In the monologues-descriptions of comic strips by H. Bidstrup “Hair loss treatment”, the cases of self-repair were annotated dividing into error correction and self-editing. The main repair operation types were revealed: word form change, word change, replacement of a word part with a word, word insertion, and phrase reformulation. Lexical or non-lexical repair initiators were also tagged—breaks, hesitation pauses, vocalized pauses, prolongations of sounds, discourse markers, etc. The study shows that self-repairs in general are more frequent in introverts’ speech. The prevalent type among all the speakers is self-editing, however, error correction is used more often by introverts. The most frequent repair operations in introverts’ speech are replacement of a word part with a word and word form change. Extraverts’ most frequent type of repair operations is word change. Introverts usually initiate repairs with breaks, silent and vocalized pauses, extraverts—more often with silent pauses, ambiverts—with breaks and silent pauses. Thus, self-repair initiation and type of operation is, in some cases, connected with the psychological characteristics of the speaker.
Mobile Object Detection Without Labels with Camera-LiDAR Fusion for Roadside Perception

In the vehicle-road collaboration scenario, the model performance fully rely on high-quality human-annotated data in the extensive deployment of roadside. But the cost of humanannotated labels is expensive. In this paper, we propose a novel mobile object detection method which can generate high accurate 3D object labels from unlabeled point could and images. The method mainly consists of two modules: First, we leverage combination of ephemeral points from point cloud and optical flow map from image to obtain initial labels, then we use these initial labels to train a high-precision detector via several self-training. The experimental results show that our method can effectively train a high accurate mobile object detector without relying on any manual labeling.
“Plain and Natural” Versus “Accurate and Unambiguous”: Pronominal Intrasentential Anaphora in Russian Legislative Texts

The paper deals with pronominal anaphora in Russian legislative texts. A sample of 1,917 sentences from the synchronous legal corpus with single usage of 3rd person personal pronouns has been examined. The instances of discourse and intrasentential anaphora have been distinguished. The set of sentences with intrasentential anaphora has been described using the following parameters: the type of referent, the length of antecedent in words, the type of syntactic phrase of antecedent, the linear distance between antecedent and anaphor. The main results are as follows. The proportion of discourse anaphora instances is about 1.4%, that is, the authors of Russian legislative texts tend to have the antecedent and the anaphor within the same sentence. In addition, 61.66% of the pronominal anaphors are placed in the same clause with their antecedents. Thus, Russian legal drafters indeed fear generating referentially ambiguous, difficult-to-interpret passages and tend to “keep the antecedent and the anaphor close to each other”.
Military Web Information Extraction Incorporating Resource Metadata Distribution

We analyze the relationship between the metadata distribution of military resources and the distribution of the web content in news, events, and resources in three major sections of military web stations, and thus we propose a method for extracting information from military web pages by integrating the metadata distribution and the basic features of military web pages to increase the difference between the content density and the noisy text density, to extract the content of military web pages. To verify the practical effectiveness of the proposed method in extracting information from military web pages, 69,379 web pages in the three major sections were randomly selected for the experiments in this work. The experimental results show that compared with the CETR, VIPS, SRV, and WNISK algorithms, the proposed method in this paper achieves better content extraction performance, with F-values of 98.21%, 97.11%, and 97.46% for the extraction of web page information in the three major sections respectively.