Reviewerly/exHarmony huggingface dataset:
Comma Selection Outperforms Plus Selection on OneMax with Randomly Planted Optima

It is an ongoing debate whether and how comma selection in evolutionary algorithms helps to escape local optima. We propose a new benchmark function to investigate the benefits of comma selection: OneMax with randomly planted local optima, generated by frozen noise. We show that comma selection (the (1, Λ) EA) is faster than plus selection (the (1 + Λ) EA) on this benchmark, in a fixed-target scenario, and for offspring population sizes Λ for which both algorithms behave differently. For certain parameters, the (1, Λ) EA finds the target in Θ(n ln n) evaluations, with high probability (w.h.p.), while the (1 + Λ) EA w.h.p. requires almost Θ((n ln n)2) evaluations.
An Effective One-Shot Neural Architecture Search Method with Supernet Fine-Tuning for Image Classification

Neural architecture search (NAS) is becoming increasingly popular for its ability to automatically search for an appropriate network architecture, avoiding laborious manual designing processes, and potentially introducing novel structures. However, many NAS methods suffer from heavy computational consumption. One-shot NAS alleviates this issue by training a big supernet and allowing all the candidates to inherit weights from the supernet, avoiding training from scratch. However, the performance evaluations in one-shot methods might not always be reliable due to the weight co-adaption issue inside the supernet. This paper proposes a super-net fine-tuning strategy to allow the supernet's weights to adapt to the new focused search region along with the evolutionary process. Furthermore, a new genetic algorithm-based search method is designed to offer an effective path-sampling strategy in the search region and provide a new population generation method to preclude unfair fitness comparisons between different populations. The experimental results demonstrate the proposed method achieves promising results compared with 32 peer competitors in terms of the algorithm's computational cost and the searched architecture's performance. Specifically, the proposed method achieves classification error rates of 2.50% and 17.07% within only 0.50 and 0.92 GPU-days on CIFAR10 and CIFAR100, respectively.
Seeing things the same way: perspectives and lessons learned from research-design collaborations

Abstract Information visualizations are increasingly being developed by informatics researchers to communicate health information to lay audiences. For high-quality results, it is advisable to collaborate with creative professionals such as graphic designers, illustrators, or user interface/user experience designers. However, such collaborations are often a novel experience for both parties, each of which may be unfamiliar with the needs and processes of the other. We have coalesced our experiences from both the research and design perspectives to offer practical guidance in hopes of promoting the success of future collaborations. We offer suggestions for determining design needs, communicating with design professionals, and carrying out the design process. We assert that successful collaborations are predicated on careful and intentional planning at the outset of a project, a thorough understanding of each party’s scope expertise, clear communication, and ample time for the design process to unfold.
Approaching capacity limits in photon-starved noisy optical communication

We show that utilizing pulse position modulation and photon number resolving detectors together with techniques such as quantum pulse gating allows to approach ultimate quantum limit on optical communication capacity in the presence of background noise in the weak output power regime.This shows that for communication over long distances by means of current existing technology it is possible to attain optimal performance, limited only by laws of quantum mechanics.
DDGAN: Dense Residual Module and Dual-stream Attention-Guided Generative Adversarial Network for colorizing near-infrared images

Transforming near-infrared(NIR) images into realistic RGB images is a challenging task. Recently, with the development of deep learning, the colorization of NIR images has been significantly improved. However, there are still problems with distorted textures and blurred details. The main reason is that NIR images require color prediction and gray-scale detail recovery to be colorized. Moreover, NIR images contain limited detail and lack color information, which poses a serious challenge for feature extraction. To address the problems, we propose the Dense Residual Module and Dual-stream Attention-Guided Generative Adversarial Network (DDGAN) in this paper. The Dense Residual Module (DRM) improves the network feature extraction capability in the form of dense residuals and increases the network depth to improve the adaptability of the network. The Dual-stream Attention Module (DAM) further improves the quality of colorized images by enhancing important features and suppressing unnecessary features to focus on essential visual features. We propose a composite loss function consisting of content loss, adversarial loss, perceptual loss, synthesized loss, and total variation loss, which improves the quality of colorful images in terms of both edge structure and visual perception. We consider the NIR images for the visible image conversion task to evaluate the efficiency and performance of the proposed model. The proposed DDGAN outperforms most existing methods in terms of efficiency and quality of the generated images on the RGB–NIR dataset and the OMSIV dataset. Compared to state-of-the-art methods, the proposed DDGAN shows promising results with significant improvements in PSNR, SSIM, MSE, and NRMSE. Extensive experimental data show that the proposed DDGAN can produce state-of-the-art colorized NIR images in objective metrics and subjective quality.
A Survey of Recent Developments in Testability, Safety and Security of RISC-V Processors

With the continued success of the open RISC-V architecture, practical deployment of RISC-V processors necessitates an in-depth consideration of their testability, safety and security aspects. This survey provides an overview of recent developments in this quickly-evolving field. We start with discussing the application of state-of-the-art functional and system-level test solutions to RISC-V processors. Then, we discuss the use of RISC-V processors for safety-related applications; to this end, we outline the essential techniques necessary to obtain safety both in the functional and in the timing domain and review recent processor designs with safety features. Finally, we survey the different aspects of security with respect to RISC-V implementations and discuss the relationship between cryptographic protocols and primitives on the one hand and the RISC-V processor architecture and hardware implementation on the other. We also comment on the role of a RISC-V processor for system security and its resilience against side-channel attacks.
Towards a Secure and Scalable Access Control System Using Blockchain

Access control, both physical and virtual, has always been a crucial aspect in maintaining the security of corporate information systems. Recently, several solutions have been proposed to address physical and virtual access control, including the use of electronic badges that can be costly to produce and easily misplaced. Additionally, numerous, sometimes expensive, cloud-based solutions have also been adopted. However, these solutions are often provided by third-party organizations, which require entities to place trust in these providers, a risk that is unacceptable for industries such as the military and banking. To solve this issue, we propose a novel Blockchain-based solution to establish a scalable and secure system for managing access controls. Blockchain offers a secure, decentralized, and most importantly, immutable alternative that eliminates the need for trust in third-party providers. We have implemented, tested, and deployed our Blockchain-based access control architecture on the Avalanche official network. The results demonstrate that this solution offers strong security, flexibility, efficiency, and cost-effectiveness, making it a promising approach to mitigate Distributed Denial of Service (DDoS) attacks in the Internet of Things (IoT). Our deployment on the Avalanche network confirms the feasibility and robustness of our approach in a real-world setting.
Data Analytics on Blockchains

In recent years, blockchains have been exploited in areas way beyond finance, enabling numerous innovative usage scenarios and applications. However, the extension of the existing systems and applications in order to support data persistence on a blockchain is time-consuming. Therefore, this paper proposes a model-driven based approach leveraging smart contracts with the goal to automate data persistence on blockchains. The approach is evaluated in data analytics use cases. According to our results, the proposed approach fully automates the data import and export processes without negatively affecting the predictive power of the models trained using data coming from the blockchain.
Fast Computation of Multi-Scalar Multiplication for Pairing-Based zkSNARK Applications

The operation of computing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> scalar multiplications in an elliptic curve group and then adding them together is called n-scalar multiplication. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> -scalar multiplication is the essential operation for proof generation and verification in pairing-based trusted setup zero-knowledge succinct non-interactive argument of knowledge protocols, which enable the privacy-preserving features in blockchain applications. This paper proposed a method to compute <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> -scalar multiplication taking advantage of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3n$</tex> precomputed points. When instantiating over BLS12-381 curve, for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n=2^{c}\ (10\leq c\leq 22)$</tex> , which covers the majority of our purported applications, the proposed method showed 2.59% ∼ 12.26% theoretical speed improvement and demonstrated 1.63% ∼ 11.54% experimental improvement against Pippenger's bucket method.
DACOS—A Manually Annotated Dataset of Code Smells

Researchers apply machine-learning techniques for code smell detection to counter the subjectivity of many code smells. Such approaches need a large, manually annotated dataset for training and benchmarking. Existing literature offers a few datasets; however, they are small in size and, more importantly, do not focus on the subjective code snippets. In this paper, we present DACOS, a manually annotated dataset containing 10, 267 annotations for 5, 192 code snippets. The dataset targets three kinds of code smells at different granularity–multifaceted abstraction, complex method, and long parameter list. The dataset is created in two phases. The first phase helps us identify the code snippets that are potentially subjective by determining the thresholds of metrics used to detect a smell. The second phase collects annotations for potentially subjective snippets. We also offer an extended dataset DACOSX that includes definitely benign and definitely smelly snippets by using the thresholds identified in the first phase. We have developed TAGMAN, a web application to help annotators view and mark the snippets one-by-one and record the provided annotations. We make the datasets and the web application accessible publicly. This dataset will help researchers working on smell detection techniques to build relevant and context-aware machine-learning models.
ESE: Efficient Security Enhancement Method for the Secure Aggregation Protocol in Federated Learning

In federated learning, a parameter server may actively infer sensitive data of users and a user may arbitrarily drop out of a learning process. Bonawitz et al. propose a secure aggregation protocol for federated learning against a semi-honest adversary and a security enhancement method against an active adversary at ACM CCS 2017. The purpose of this paper is to analyze their security enhancement method and to design an alternative. We point out that their security enhancement method has the risk of Eclipse attack and that the consistency check round in their method could be removed. We give a new efficient security enhancement method by redesigning an authentication message and by adjusting the authentication timing. The new method produces an secure aggregation protocol against an active adversary with less communication and computation costs.
Toward Automated Tools to Support Ethical GUI Design

Past studies have illustrated the prevalence of UI dark patterns, or user interfaces that can lead end-users toward (unknowingly) taking actions that they may not have intended. Such deceptive UI designs can be either intentional (to benefit an online service) or unintentional (through complicit design practices) and can result in adverse effects on end users, such as oversharing personal information or financial loss. While significant research progress has been made toward the development of dark pattern taxonomies across different software domains, developers and users currently lack the guidance to help recognize, avoid, and navigate these often subtle design motifs. However, automated recognition of dark patterns is a challenging task, as the instantiation of a single type of pattern can take many forms, leading to significant variability. This paper depicts promising results from preliminary work that inspire towards addressing this problem and proposes a comprehensive research agenda that is aimed at implementing a developer-facing automated tool to support ethical GUI design.
Do Subjectivity and Objectivity Always Agreeƒ A Case Study with Stack Overflow Questions

In Stack Overflow (SO), the quality of posts (i.e., questions and answers) is subjectively evaluated by users through a voting mechanism. The net votes (upvotes − downvotes) obtained by a post are often considered an approximation of its quality. However, about half of the questions that received working solutions got more downvotes than upvotes. Furthermore, about 18% of the accepted answers (i.e., verified solutions) also do not score the maximum votes. All these counter-intuitive findings cast doubts on the reliability of the evaluation mechanism employed at SO. Moreover, many users raise concerns against the evaluation, especially downvotes to their posts. Therefore, rigorous verification of the subjective evaluation is highly warranted to ensure a non-biased and reliable quality assessment mechanism. In this paper, we compare the subjective assessment of questions with their objective assessment using 2.5 million questions and ten text analysis metrics. According to our investigation, four objective metrics agree with the subjective evaluation, two do not agree, one either agrees or disagrees, and the remaining three neither agree nor disagree with the subjective evaluation. We then develop machine learning models to classify the promoted and discouraged questions. Our models outperform the state-of-the-art models with a maximum of about 76%–87% accuracy.
LLMSecEval: A Dataset of Natural Language Prompts for Security Evaluations

Large Language Models (LLMs) like Codex are powerful tools for performing code completion and code generation tasks as they are trained on billions of lines of code from publicly available sources. Moreover, these models are capable of generating code snippets from Natural Language (NL) descriptions by learning languages and programming practices from public GitHub repositories. Although LLMs promise an effortless NL-driven deployment of software applications, the security of the code they generate has not been extensively investigated nor documented. In this work, we present LLMSecEval, a dataset containing 150 NL prompts that can be leveraged for assessing the security performance of such models. Such prompts are NL descriptions of code snippets prone to various security vulnerabilities listed in MITRE’s Top 25 Common Weakness Enumeration (CWE) ranking. Each prompt in our dataset comes with a secure implementation example to facilitate comparative evaluations against code produced by LLMs. As a practical application, we show how LLMSecEval can be used for evaluating the security of snippets automatically generated from NL descriptions.
DHTee: Decentralized Infrastructure for Heterogeneous TEEs

Trusted execution environment (TEE) technology has many uses, such as protecting data in the cloud and improving security for industrial IoT. However, there are technical challenges that limit its widespread adoption. These challenges include the fact that different TEE vendors have incompatible solutions, and devices equipped with the same TEE technology may belong to different owners, making it difficult to establish trust between them. To address these challenges and fully utilize TEE technology, a decentralized coordination mechanism called DHTee is proposed. DHTee uses blockchain technology to support key TEE functions in a heterogeneous TEE environment, especially attestation service. Devices equipped with TEE can interact securely with the blockchain to determine whether potential collaborating devices meet the requirements. DHTee is also flexible and can support new TEE schemes without affecting existing TEEs.
DEEPER: Enhancing Liquidity in Concentrated Liquidity AMM DEX via Sharing

This paper presents Deeper, a design for a decentralized exchange that enhances the average active liquidity via reserve sharing. By doing this, it addresses the problem of shallow liquidity in low trading volume token pairs. Deeper allows liquidity providers of multiple trading pairs against a common token to share liquidity. This is achieved by creating a common reserve pool for the shared token that is accessible by each trading pair. Independent from the shared liquidity, providers are free to add liquidity to individual token pairs without any restriction. The trading between one token pair does not affect the price of other token pairs even though the reserve of the shared token changes. The proposed design is an extension of concentrated liquidity market maker-based DEXs that is simple enough to be implemented on smart contracts. Experiments show that for a batch consisting of 8 trading pairs, Deeper enhances liquidity by over 2.6 − 5.9 ×. This enhancement in liquidity can be increased further by increasing participating tokens in the shared pool.
An Intelligent Duplicate Bug Report Detection Method Based on Technical Term Extraction

As the bug description data generated during the software maintenance cycle, bug reports are usually hastily written by different users, resulting in many redundant and duplicate bug reports (DBRs). Once the DBRs are repeatedly assigned to developers, it will inevitably lead to a serious waste of human resources, especially for large-scale open-source projects. Recently, many experts and scholars have devoted themselves to researching the detection of DBRs and put forward a series of detection methods for DBRs. However, there is still much room for improvement in the performance of DBR prediction. Therefore, this paper proposes a new method for detecting DBR based on technical term extraction, CTEDB (Combination of Term Extraction and DeBERTaV3) for short. This method first extracts technical terms from the text information of bug reports based on Word2Vec and TextRank algorithms. Then it calculates the semantic similarity of technical terms between different bug reports by combining Word2Vec and SBERT models. Finally, it completes the DBR detection task by combining the DeBERTaV3 model. The experimental results show that CTEDB has achieved good results in detecting DBR, and has obviously improved the accuracy, F1-score, recall and precision compared with the baseline approaches.
GiveMeLabeledIssues: An Open Source Issue Recommendation System

Developers often struggle to navigate an Open Source Software (OSS) project’s issue-tracking system and find a suitable task. Proper issue labeling can aid task selection, but current tools are limited to classifying the issues according to their type (e.g., bug, question, good first issue, feature, etc.). In contrast, this paper presents a tool (GiveMeLabeledIssues) that mines project repositories and labels issues based on the skills required to solve them. We leverage the domain of the APIs involved in the solution (e.g., User Interface (UI), Test, Databases (DB), etc.) as a proxy for the required skills. GiveMeLabeledIssues facilitates matching developers’ skills to tasks, reducing the burden on project maintainers. The tool obtained a precision of 83.9% when predicting the API domains involved in the issues. The replication package contains instructions on executing the tool and including new projects. A demo video is available at https://www.youtube.com/watch?v=ic2quUue7i8
Reputation as Contextual Knowledge: Incentives and External Value in Truthful Blockchain Oracles

The primary strengths of blockchain systems come from strong guarantees of immutability and reliability, and these systems can be extended with programmatic logic through Smart Contracts. Many such programmatic use cases would benefit from the use of external data. However, the method of bringing that data onto the blockchain needs to be trustworthy and secure. Otherwise, the benefits of blockchain, namely distributed trust, no single points of failure, and immutability, would be at risk. Blockchain oracles are proposed as a conceptual solution to this problem. A blockchain oracle acts as a trusted intermediary to external data. In this work we analyze existing proposals for Discernible event oracle mechanisms, which seek to address oracle queries with a broad knowledgeable answering population, to characterize problems related to the incentives imposed by external value that depends on oracle outcomes. In doing so, we focus on continued challenges to voting based oracles, including trust limitations and the need to match questions to knowledgeable answering parties. To address these difficulties, we propose an extension to existing oracle protocols to utilize reputation as a tool to measure value and as a tool to associate questions with context. By providing a method to track contextual knowledge, our proposal allows for context-based query matching and enables higher probability of correctness for a given population size as well as stronger participation incentives.
SREP: Out-Of-Band Sync of Transaction Pools for Large-Scale Blockchains

Synchronization of transaction pools (mempools) has shown potential for improving the performance and block propagation delay of state-of-the-art blockchains. Indeed, various heuristics have been proposed in the literature to this end, all of which incorporate exchanges of unconfirmed transactions into their block propagation protocol. In this work, we take a different approach, maintaining transaction synchronization outside (and independently) of the block propagation channel. In the process, we formalize the synchronization problem within a graph theoretic framework and introduce a novel algorithm (SREP - Set Reconciliation-Enhanced Propagation) with quantifiable guarantees. We analyze the algorithm's performance for various realistic network topologies, and show that it converges on any connected graph in a number of steps that is bounded by the diameter of the graph. We confirm our analytical findings through extensive simulations that include comparison with MempoolSync, a recent approach from the literature. Our simulations show that SREP incurs reasonable overall bandwidth overhead and, unlike MempoolSync, scales gracefully with the size of the network.