Reviewerly/exHarmony huggingface dataset:
Mitigation of liveness attacks in DAG-based ledgers

The robust construction of the ledger data structure is an essential ingredient for the safe operation of a distributed ledger. While in traditional linear blockchain systems, permission to append to the structure is leader-based, in Directed Acyclic Graph-based ledgers, the writing access can be organised leaderless. However, this leaderless approach relies on fair treatment of non-referenced blocks, i.e. tips, by honest block issuers. We study the impact of a deviation from the standard tip selection by a subset of block issuers with the aim of halting the confirmation of honest blocks entirely. W e provide models on this so-called orphanage of blocks and validate these through open-sourced simulation studies. A critical threshold for the adversary issuance rate is shown to exist, above which the tip pool becomes unstable, while for values below the orphanage decrease exponentially. We study the robustness of the protocol with an expiration time on tips, also called garbage collection, and modification of the parent references per block.
LicenseRec: Knowledge based Open Source License Recommendation for OSS Projects

Open Source license is a prerequisite for open source software, which regulates the use, modification, redistribution, and attribution of the software. Open source license is crucial to the community development and commercial interests of an OSS project, yet choosing a proper license from hundreds of licenses remains challenging. Tools assisting developers to understand the terms and pick the right license have been emerging, while inferring license compatibility on the dependency tree and satisfying the complex needs of developers are beyond the capability of most of them. Thus we propose LicenseRec, an open source license recommendation tool that helps to bridge the gap. LicenseRec performs fine-grained license compatibility checks on OSS projects' code and dependencies, and assists developers to choose the optimal license through an interactive wizard with guidelines of three aspects: personal open source style, business pattern, and community development. The usefulness of Li-censeRec is confirmed by the consistent positive feedback from 10 software developers with academic and industrial backgrounds. Our tool is accessible at https://licenserec.com and a video showcasing the tool is available at https://video.licenserec.com.
Cobalt: Optimizing Mining Rewards in Proof-of-Work Network Games

Mining in proof-of-work blockchains has become an expensive affair requiring specialized hardware capable of executing several megahashes per second at huge electricity costs. Miners earn a reward each time they mine a block within the longest chain, which helps offset their mining costs. It is therefore of interest to miners to maximize the number of mined blocks in the blockchain and increase revenue. A key factor affecting mining rewards earned is the connectivity between miners in the peer- to- peer network. To maximize rewards a miner must choose its network connections carefully, ensuring existence of paths to other miners that are on average of a lower latency compared to paths between other miners. We formulate the problem of deciding whom to connect to for miners as a combinatorial bandit problem. Each node picks its neighbors strategically to minimize the latency to reach 90% of the hash power of the network relative to the 90-th percentile latency from other nodes. A key contribution of our work is the use of a network coordinates based model for learning the network structure within the bandit algorithm. Experimentally we show our proposed algorithm outperforming or matching baselines on diverse network settings.
DGMF: Fast Generation of Comparable, Updatable Dependency Graphs for Software Repositories

Dependency graphs for software repositories have been utilized in a variety of different research contexts. However, to this date there is no unified data model for such graphs, often prompting researchers to implement domain-specific methodologies from scratch. This greatly hinders comparability and makes it hard to incorporate existing tooling into new contexts. With this work we propose DGMF, a framework for mining dependency graphs via repository-specific, user-defined adapters. DGMF is designed to be fast, to require little repository-specific code, and to produce graphs that are comparable even across different repositories. We present our design and implementation, as well as three predefined adapters and an evaluation.
Tell Me Who Are You Talking to and I Will Tell You What Issues Need Your Skills

Selecting an appropriate task is challenging for newcomers to Open Source Software (OSS) projects. To facilitate task selection, researchers and OSS projects have leveraged machine learning techniques, historical information, and textual analysis to label tasks (a.k.a. issues) with information such as the issue type and domain. These approaches are still far from mainstream adoption, possibly because of a lack of good predictors. Inspired by previous research, we advocate that label prediction might benefit from leveraging metrics derived from communication data and social network analysis (SNA) for issues in which social interaction occurs. Thus, we study how these "social metrics" can improve the automatic labeling of open issues with API domains—categories of APIs used in the source code that solves the issue—which the literature shows that newcomers to the project consider relevant for task selection. We mined data from OSS projects’ repositories and organized it in periods to reflect the seasonality of the contributors’ project participation. We replicated metrics from previous work and added social metrics to the corpus to predict API-domain labels. Social metrics improved the performance of the classifiers compared to using only the issue description text in terms of precision, recall, and F-measure. Precision (0.922) increased by 15.82% and F-measure (0.942) by 15.89% for a project with high social activity. These results indicate that social metrics can help capture the patterns of social interactions in a software project and improve the labeling of issues in an issue tracker.
Picaso: Enhancing API Recommendations with Relevant Stack Overflow Posts

While having options could be liberating, too many options could lead to the sub-optimal solution being chosen. This is not an exception in the software engineering domain. Nowadays, API has become imperative in making software developers’ life easier. APIs help developers implement a function faster and more efficiently. However, given the large number of open-source libraries to choose from, choosing the right APIs is not a simple task. Previous studies on API recommendation leverage natural language (query) to identify which API would be suitable for the given task. However, these studies only consider one source of input, i.e., GitHub or Stack Overflow, independently. There are no existing approaches that utilize Stack Overflow to help generate better API sequence recommendations from queries obtained from GitHub. Therefore, in this study, we aim to provide a framework that could improve the result of the API sequence recommendation by leveraging information from Stack Overflow. In this work, we propose Picaso, which leverages contrastive learning to train a sentence embedding model and a cross-encoder model to build a classification model in order to find a semantically similar Stack Overflow post given an annotation (i.e., code comment). Subsequently, Picaso then uses the Stack Overflow’s title as a query expansion. Picaso then uses the extended queries to fine-tune a CodeBERT, resulting in an API sequence generation model. Based on our experiments, we found that incorporating the Stack Overflow information into CodeBERT would improve the performance of API sequence generation’s BLEU-4 score by 10.8%.
ComBo: A Novel Functional Bootstrapping Method for Efficient Evaluation of Nonlinear Functions in the Encrypted Domain

The application of Fully Homomorphic Encryption (FHE) to privacy issues arising in inference or training of neural networks has been actively researched over the last few years. Yet, although practical performances have been demonstrated on certain classes of neural networks, the inherent high computational cost of FHE operators has prevented the scaling capabilities of FHE-based encrypted domain inference to the large and deep networks used to deliver advanced classification functions such as image interpretation tasks. To achieve this goal, a new hope is coming from TFHE functional bootstrapping which, rather than being just used for refreshing ciphertexts (i.e., reducing their noise level), can be used to evaluate operators which are difficult to express as low complexity arithmetic circuits, at no additional cost. In this work, we first propose ComBo (Composition of Bootstrappings) a new full domain functional bootstrapping method with TFHE for evaluating any function of domain and codomain the real torus $$\mathbb {T}$$ by using a small number of bootstrappings. This result improves on previous approaches: like them, we allow for evaluating any functions, but with error rates reduced by a factor of up to $$2^{80}$$ . This claim is supported by a theoretical analysis of the error rate of other functional bootstrapping methods from the literature. The paper is concluded by extensive experimental results demonstrating that our method achieves better performances in terms of both time and precision, in particular for the Rectified Linear Unit (ReLU) function, a nonlinear activation function commonly used in neural networks. As such, this work provides a fundamental building-block towards scaling the homomorphic evaluation of neural networks over encrypted data.
ROI-BMC-DNNet: An efficient automatic analysis model of whole-slide scanned bone marrow aspirate images for the diagnosis of hematological disorders

Cell differential counting in the scanned whole-slide image (WSI) is essential but still challenging in the intelligent diagnosis of hematological disorders based on bone marrow aspirate smear images. The huge size and the complex content of WSI pose multiple obstacles to training an efficient and accurate analysis model. This paper proposes a novel analysis framework termed ROI-BMC-DNNet. To capture patch images in the cell well-spread areas, we provide a pyramid segmentation network based on prior pathological knowledge to automatically locate regions of interest (ROI) that pathologists usually manually focus on to count nucleated cells. Specifically, we describe a simple but effective alignment method for multiscale ROI, aiming to maintain the accuracy of the semantic and the spatial information of the pyramid network. Furthermore, we design a patch sampling algorithm and a patch quality evaluating network to ensure the effectiveness of the sampled patches. Additionally, we provide a cell detection model to realize the automatic differential counting of the nucleated cells in the sampled patches. Experiments are conducted on the datasets of scanned WSIs from 120 patients. Our method achieves 92.27% retrieval precision for the high-quality patches (patches with nucleated cells with better morphology and without particles). When the missed nucleated cells are evaluated, the detection Recall and Precision of the bone marrow nucleated cells from the WSIs are 87.13% and 87.90% respectively. Notably, the recognition Recall and Precision of the blast cell are 91.37% and 90.47%, respectively. The results confirm that the proposed method is promising to work as an auxiliary diagnostic tool for hematological disorders.
Vocal accommodation to technology: the role of physical form

This study examines participants' vocal accommodation toward text-to-speech (TTS) voices produced by three devices, varying in the extent to which they embody a human form. Thirty eight speakers shadowed words produced by a male and female TTS voice presented across three physical forms: an Amazon Echo smart speaker (least human-like), Nao robot (slightly more human-like), and a Furhat robot (more human-like). Ninety-six independent raters completed a separate AXB perceptual similarity assessment, which provides a holistic evaluation of accommodation. Results show convergence to the voices across all physical forms; convergence is even stronger toward the female TTS voice when presented with the Echo smart speaker form in the female TTS voice, consistent with participants' higher rated likability and lower creepiness of the Echo. We interpret our findings through the lens of communication accommodation theory (CAT), providing support for accounts of speech communication and human–computer interaction frameworks.
Generation of virtual digital human for customer service industry

With the widespread development of digital technology, individuals’ daily activities are inseparable from interaction with electronic devices. Researchers have become interested in developing novel methods, to enable users to experience social and emotional satisfaction that traditional face-to-face interaction provides. In this study, we propose a novel deep learning-based pipeline to generate virtual digital humans for customer service industry. Specifically, we propose a method to construct a database with template service actions. Furthermore, we propose a two-stage method for generating 2D virtual human videos with gestures and emotional lip-sync expressions. We have conducted qualitative and quantitative experiments on the proposed 2D virtual human video generation method. The results demonstrate that the method effectively generates high-quality virtual digital humans for the customer service industry.
Quantum SoC and On-Chip Circuit Synthesis in the NISQ Era

In recent years, fueled by the breakthroughs in the technology in quantum computation, there has been a rising interest in the noisy intermediate-scale quantum (NISQ) era. In addition to a large number of qubits, the study of error correction and quantum algorithms has made great progress. However, as a primary goal of quantum computation, making practicable quantum computers in the NISQ era still needs further study, mainly focusing on quantum computer organization, architecture, and circuit synthesis. This paper studies the quantum circuit synthesis in a small-scale universal quantum computing device called a “quantum system-on-chip” (QSoC). We analyze the quantum compilation of the hybrid architecture for a small-scaled universal quantum computational device with a specific size (quantum chip). Two kinds of on-chip circuit synthesis algorithms are proposed and discussed.
Quantum optimisation for continuous multivariable functions by a structured search

Abstract Solving optimisation problems is a promising near-term application of quantum computers. Quantum variational algorithms (QVAs) leverage quantum superposition and entanglement to optimise over exponentially large solution spaces using an alternating sequence of classically tunable unitaries. However, prior work has primarily addressed discrete optimisation problems. In addition, these algorithms have been designed generally under the assumption of an unstructured solution space, which constrains their speedup to the theoretical limits for the unstructured Grover’s quantum search algorithm. In this paper, we show that QVAs can efficiently optimise continuous multivariable functions by exploiting general structural properties of a discretised continuous solution space with a convergence that exceeds the limits of an unstructured quantum search. We present the quantum multivariable optimisation algorithm and demonstrate its advantage over pre-existing methods, particularly when optimising high-dimensional and oscillatory functions.
pyribs: A Bare-Bones Python Library for Quality Diversity Optimization

Recent years have seen a rise in the popularity of quality diversity (QD) optimization, a branch of optimization that seeks to find a collection of diverse, high-performing solutions to a given problem. To grow further, we believe the QD community faces two challenges: developing a framework to represent the field's growing array of algorithms, and implementing that framework in software that supports a range of researchers and practitioners. To address these challenges, we have developed pyribs, a library built on a highly modular conceptual QD framework. By replacing components in the conceptual framework, and hence in pyribs, users can compose algorithms from across the QD literature; equally important, they can identify unexplored algorithm variations. Furthermore, pyribs makes this framework simple, flexible, and accessible, with a user-friendly API supported by extensive documentation and tutorials. This paper overviews the creation of pyribs, focusing on the conceptual framework that it implements and the design principles that have guided the library's development. Pyribs is available at https://pyribs.org
Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models

We present a model-agnostic framework for jointly optimizing the predictive performance and interpretability of supervised machine learning models for tabular data. Interpretability is quantified via three measures: feature sparsity, interaction sparsity of features, and sparsity of non-monotone feature effects. By treating hyperparameter optimization of a machine learning algorithm as a multi-objective optimization problem, our framework allows for generating diverse models that trade off high performance and ease of interpretability in a single optimization run. Efficient optimization is achieved via augmentation of the search space of the learning algorithm by incorporating feature selection, interaction and monotonicity constraints into the hyperparameter search space. We demonstrate that the optimization problem effectively translates to finding the Pareto optimal set of groups of selected features that are allowed to interact in a model, along with finding their optimal monotonicity constraints and optimal hyperparameters of the learning algorithm itself. We then introduce a novel evolutionary algorithm that can operate efficiently on this augmented search space. In benchmark experiments, we show that our framework is capable of finding diverse models that are highly competitive or outperform state-of-the-art XGBoost or Explainable Boosting Machine models, both with respect to performance and interpretability.
Recursive Feature Elimination Based Feature Selection in Modulation Classification for MIMO Systems

The feature-based (FB) algorithms are widely used in modulation classification due to their low complexity. As a prerequisite step of FB, feature selection can reduce the computational complexity without significant performance loss. In this paper, according to the linear separability of cumulant features, the hyperplane of the support vector machine is used to classify modulation types, and the contribution of different features is ranked through the weight vector. Then, cumulant features are selected using recursive feature elimination (RFE) to identify the modulation type employed at the transmitter. We compare the performance of the proposed algorithm with existing feature selection algorithms and analyze the complexity of all the mentioned algorithms. Simulation results verify that the proposed RFE algorithm can optimize the selection of the features to realize modulation recognition and improve identification efficiency.
Attention Guided Enhancement Network for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation using only image-level labels is critical since it alleviates the need for expensive pixel-level labels.Most cuttingedge methods adopt two-step solutions that learn to produce pseudo-ground-truth using only image-level labels and then train off-the-shelf fully supervised semantic segmentation network with these pseudo labels.Although these methods have made significant progress, they also increase the complexity of the model and training.In this paper, we propose a one-step approach for weakly supervised image semantic segmentation -attention guided enhancement network (AGEN), which produces pseudopixel-level labels under the supervision of image-level labels and trains the network to generate segmentation masks in an end-to-end manner.Particularly, we employ class activation maps (CAM) produced by different layers of the classification branch to guide the segmentation branch to learn spatial and semantic information.However, the CAM produced by the lower layer can capture the complete object region but with many noises.Thus, the self-attention module is proposed to enhance object regions adaptively and suppress irrelevant object regions, further boosting the segmentation performance.Experiments on the Pascal VOC 2012 dataset demonstrate that AGEN outperforms alternative state-of-the-art weakly supervised semantic segmentation methods exclusively relying on image-level labels.
A General Purpose Representation and Adaptive EA for Evolving Graphs

Graphs are a way to describe complex entities and their relations that apply to many practically relevant domains. However, domains often differ not only in the properties of nodes and edges, but also in the constraints imposed to the overall structure. This makes hard to define a general representation and genetic operators for graphs that permit the evolutionary optimization over many domains. In this paper, we tackle this challenge. We first propose a representation template that can be customized by users for specific domains: the constraints and the genetic operators are given in Prolog, a declarative programming language for operating with logic. Then, we define an adaptive evolutionary algorithm that can work with a large number of genetic operators by modifying their usage probability during the evolution: in this way, we relieve the user from the burden of selecting in advance only operators that are "good enough". We experimentally evaluate our proposal on two radically different domains to demonstrate its applicability and effectiveness: symbolic regression with trees and text extraction with finite-state automata. The results are promising: our approach does not trade effectiveness for versatility and is not worse than other domain-tailored approaches.
Genetic Algorithm with Linkage Learning

Next-generation genetic algorithms (GAs) should explore information from the problem structure whenever possible. Variable interactions can be inferred using linkage learning. Statistical linkage learning techniques were shown to improve GAs' effectiveness significantly in many problems, but may eventually report false linkages. On the other hand, empirical linkage learning (ELL) techniques discover only true variable dependencies. However, traditional ELL techniques are computationally expensive. We introduce the genetic algorithm with linkage learning (GAwLL), which discovers an empirical weighted variable interaction graph (VIGw) as a side-effect of the optimization performed by a GA, making it a no-cost ELL technique. Vertices of the VIGw represent decision variables and weights indicate the strength of the interaction between variables. The VIGw allows us to obtain new insights about the optimization problem and can be used to design genetic operators that efficiently explore the information about variable dependencies. Experiments with NK landscapes show that GAwLL is able to efficiently build the empirical VIGw. We also present an interesting machine learning application, where the VIGw represents a feature interaction network. By using GAwLL, the feature interaction network is built as a side-effect of evolutionary feature selection.