Reviewerly/exHarmony huggingface dataset:
iTrace-Toolkit: A Pipeline for Analyzing Eye-Tracking Data of Software Engineering Studies

iTrace is community eye-tracking infrastructure that enables conducting eye-tracking studies within an Integrated Development Environment (IDE). It consists of a set of tools for gathering eye-tracking data on large real software projects within an IDE during studies on source code. Once the raw eye-tracking data is collected, processing is necessary before it can be used for analysis. Rather than provide the raw data for researchers to analyze and write their own customize scripts, we introduce iTrace-Toolkit - a suite of tools that assists with combining different data files generated from iTrace and its IDE plugins (namely Visual Studio, Atom, and Eclipse). iTrace-Toolkit also provides the crucial mapping of the valid raw eye-tracking data to source code tokens and finally generates fixations (an important metric in eye-tracking for comprehension) using three commonly used algorithms based on distance and velocity of eye movements. iTrace-Toolkit keeps track of all participant data and tasks during a given study and produces a complete lightweight database of the raw, mapped, and fixation data that is standardized and ready to be used by statistical tools. A simple GUI interface is provided for quick access to filter the data after an eye-tracking study. iTrace-Toolkit also allows for the export of the data or subset of the data to text formats for further statistical processing. YouTube Video: https://www.youtube.com/watchv9j20sOANh8w
Semantically-enriched Jira Issue Tracking Data

Current state of practice dictates that software developers host their projects online and employ project management systems to monitor the development of product features, keep track of bugs, and prioritize task assignments. The data stored in these systems, if their semantics are extracted effectively, can be used to answer several interesting questions, such as finding who is the most suitable developer for a task, what the priority of a task should be, or even what is the actual workload of the software team. To support researchers and practitioners that work towards these directions, we have built a system that crawls data from the Jira management system, performs topic modeling on the data to extract useful semantics and stores them in a practical database schema. We have used our system to retrieve and analyze 656 projects of the Apache Software Foundation, comprising data from more than a million Jira issues.
DeFaaS: Decentralized Function-as-a-Service for Emerging dApps and Web3

Function-as-a-service (FaaS) is an emerging computation architecture, which provides high scalability and flexibility. All the existing F aaS systems are owned and managed by a single cloud service provider. While this is not an issue for most existing enterprise applications, such character is not compatible with the decentralization principle of dApp/Web3 applications, more of which are being deployed in the cloud environment. Therefore, there is an urgent need to build a decentralized FaaS, which is managed by multiple cloud service providers and allows a decentralized application to take advantages of FaaS. In this research paper, we propose DeFaaS, a novel system for managing decentralized FaaS using blockchain technology and decentralized API management, where functions are executed on a distributed network of nodes by multi-cloud data centers, rather than on a centralized server. This allows for greater scalability and flexibility, as well as improved security and reliability.
Randomized Differential Testing of RDF Stores

As a special kind of graph database systems, RDF stores have been widely used in many applications, e.g., knowl-edge graphs and semantic web. RDF stores utilize SPARQL as their standardized query language to store and retrieve RDF graphs. Incorrect implementations of RDF stores can introduce logic bugs that cause RDF stores to return incorrect query results. These logic bugs can lead to severe consequences and are likely to go unnoticed by developers. However, no available tools can detect logic bugs in RDF stores. In this paper, we propose RD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , a Randomized Differential testing approach of RDF stores, to reveal discrepancies among RDF stores, which indicate potential logic bugs in RDF stores. The core idea of RD2 is to build an equivalent RDF graph for multiple RDF stores, and verify whether they can return the same query result for a given SPARQL query. Guided by the SPARQL syntax and the generated RDF graph, we automatically generate syntactically valid SPARQL queries, which can return non-empty query results with high probability. We further unify the formats of SPARQL query results from different RDF stores and find discrepancies among them. We evaluate RD2 on three popular and widely-used RDF stores. In total, we have detected 5 logic bugs in them. A video demonstration of RD2 is available at httos://youtu.be/da7XlsdbRR4.
A Large Scale Analysis of Semantic Versioning in NPM

The NPM package repository contains over two million packages and serves tens of billions of downloads per-week. Nearly every single JavaScript application uses the NPM package manager to install packages from the NPM repository. NPM relies on a "semantic versioning" (‘semver’) scheme to maintain a healthy ecosystem, where bug-fixes are reliably delivered to downstream packages as quickly as possible, while breaking changes require manual intervention by downstream package maintainers. In order to understand how developers use semver, we build a dataset containing every version of every package on NPM and analyze the flow of updates throughout the ecosystem. We build a time-travelling dependency resolver for NPM, which allows us to determine precisely which versions of each dependency would have been resolved at different times. We segment our analysis to allow for a direct analysis of security-relevant updates (those that introduce or patch vulnerabilities) in comparison to the rest of the ecosystem. We find that when developers use semver correctly, critical updates such as security patches can flow quite rapidly to downstream dependencies in the majority of cases (90.09%), but this does not always occur, due to developers’ imperfect use of both semver version constraints and semver version number increments. Our findings have implications for developers and researchers alike. We make our infrastructure and dataset publicly available under an open source license.
SoK: The Next Phase of Identifying Illicit Activity in Bitcoin

Identifying illicit behavior in the Bitcoin network is a well explored topic. The methods proposed over time have generated great insights into the deanonymization of the Bitcoin user base through the clustering of inputs and outputs. With advanced techniques being deployed by Bitcoin users, these heuristics are now being challenged in their ability to aid in the detection of illicit activity. In this SoK, we provide a comprehensive list of methods deployed by malicious actors on the network and illicit transaction mining methods. We highlight the issues associated with conducting law enforcement investigations and propose recommendations for the research community to address these issues. Our recommendations include the release of public data by exchanges to allow researchers and law enforcement to further protect the network from malicious users. We recommend the enhancement of current heuristics through machine learning methods and discuss how researchers can take the fight head-on against expert cyber criminals.
Applications of Timed-Release Encryption with Implicit Authentication

A whistleblower is a person who leaks sensitive information on a prominent individual or organisation engaging in an unlawful or immoral activity. Whistleblowing has the potential to mitigate corruption and fraud by identifying the misuse of capital. In extreme cases whistleblowing can also raise awareness about unethical practices to individuals by highlighting dangerous working conditions. Obtaining and sharing the sensitive information associated with whistleblowing can carry great risk to the individual or party revealing the data. In this paper we extend the notion of timed-release encryption to include a new security property which we term implicit authentication, with the goal of making the practice of whistleblowing safer. We formally define the new primitive of timed-release encryption with implicit authentication (TRE-IA), providing rigorous game-base definitions. We then build a practical TRE-IA construction that satisfies the security requirements of this primitive, using repeated squaring in an RSA group, and the RSA-OAEP encryption scheme. We formally prove our construction secure and provide a performance analysis of our implementation in Python along with recommendations for practical deployment and integration with an existing whistleblowing tool SecureDrop.
Efficient Computation of $$(3^n,3^n)$$-Isogenies

The parametrization of (3, 3)-isogenies by Bruin, Flynn and Testa requires over 37.500 multiplications if one wants to evaluate a single isogeny in a point. We simplify their formulae and reduce the amount of required multiplications by $$94\%$$ . Further we deduce explicit formulae for evaluating (3, 3)-splitting and gluing maps in the framework of the parametrization by Bröker, Howe, Lauter and Stevenhagen. We provide implementations to compute $$(3^n,3^n)$$ -isogenies between principally polarized abelian surfaces with a focus on cryptographic application. Our implementation can retrieve Alice’s secret isogeny in 11 s for the SIKEp751 parameters, which were aimed at NIST level 5 security.
Transferable learning on analog hardware

While analog neural network (NN) accelerators promise massive energy and time savings, an important challenge is to make them robust to static fabrication error. Present-day training methods for programmable photonic interferometer circuits, a leading analog NN platform, do not produce networks that perform well in the presence of static hardware errors. Moreover, existing hardware error correction techniques either require individual retraining of every analog NN (which is impractical in an edge setting with millions of devices), place stringent demands on component quality, or introduce hardware overhead. We solve all three problems by introducing one-time error-aware training techniques that produce robust NNs that match the performance of ideal hardware and can be exactly transferred to arbitrary highly faulty photonic NNs with hardware errors up to five times larger than present-day fabrication tolerances.
CLUE: Systems Support for Knowledge Transfer in Collaborative Learning With Neural Nets

For highly distributed environments such as edge computing, collaborative learning approaches eschew the dependence on a global, shared model, in favor of models tailored for each location. Creating tailored models for individual learning contexts reduces the amount of data transfer, while collaboration among peers provides acceptable model performance. Collaboration assumes, however, the availability of knowledge transfer mechanisms, which are not trivial for deep learning models where knowledge isn't easily attributed to precise model slices. We present CLUE – a framework that facilitates knowledge transfer for neural networks. CLUE provides new system support for dynamically extracting significant parameters from a helper node's neural network, and uses this with a multi-model boosting-based approach to improve the predictive performance of the target node. The evaluation of CLUE with different PyTorch and TensorFlow neural network models demonstrates that its knowledge transfer mechanism improves by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3.5\times$</tex-math></inline-formula> how quickly a model adapts to changes, compared to learning in isolation, while affording up to several magnitudes reduction in data movement costs compared to federated learning.
Illumination-guided inverse rendering benchmark: Learning real objects with few cameras

The realm of 3D computer vision and graphics has experienced exponential growth recently, enabling the creation of realistic virtual environments and digital representations of real-world objects. Central to this progression are 3D reconstruction methods that facilitate the virtualization of shape, color, and surface details of real objects. Current methods predominantly employ neural scene representations, which despite their efficacy, grapple with limitations such as necessitating a high number of captured images and the complexity of transforming these representations into explicit geometric forms. An alternative strategy that has gained traction is the deployment of methods such as physically-based differentiable rendering (PBDR) and inverse rendering. These approaches require fewer viewpoints, yield explicit format results, and ensure a smoother transition to other representation methods. To meaningfully assess the performance of different 3D reconstruction methods, it is imperative to utilize benchmark scenes for comparison. Despite the existence of standard objects and scenes within the literature, there is a noticeable deficiency in real-world benchmark data that concurrently captures camera, illumination, and scene parameters — all critical to high-fidelity 3D reconstructions using PBDR and inverse rendering-based methods. In this research, we introduce a methodology for capturing real-world scenes as virtual scenes, integrating illumination parameters alongside camera and scene parameters to enhance the veracity of virtual representations. In addition, we introduce a set of ten real-world scenes, along with their virtual counterparts, designed as benchmarks. These benchmarks encompass a fundamental variety of geometric constructs, including convex, concave, plain, and mixed surfaces. Additionally, we demonstrate the 3D reconstruction results of state-of-the-art 3D reconstruction methods employing PBDR in real-world scenes, using both established methodologies and our proposed one.
Mind the O˜: Asymptotically Better, but Still Impractical, Quantum Distributed Algorithms

We present two algorithms in the quantum CONGEST-CLIQUE model of distributed computation that succeed with high probability: one for producing an approximately optimal Steiner tree, and one for producing an exact directed minimum spanning tree, each of which uses O˜(n1/4) rounds of communication and O˜(n9/4) messages, achieving a lower asymptotic round and message complexity than any known algorithms in the classical CONGEST-CLIQUE model. At a high level, we achieve these results by combining classical algorithmic frameworks with quantum subroutines. Additionally, we characterize the constants and logarithmic factors involved in our algorithms as well as related classical algorithms, otherwise obscured by O˜ notation, revealing that advances are needed to render both the quantum and classical algorithms practical.
Trajectory Planning in Frenet Frame via Multi-Objective Optimization

Autonomous vehicles are an essential tool for promoting the development of intelligent transportation systems (ITS) and can effectively reduce traffic accidents caused by human errors.As an important part of the automatic driving software system, path planning is responsible for generating the motion trajectory of the vehicle, which is the primary factor determining driving quality.However, solution space construction and optimization problem formulation remain challenging research areas in the field of path planning.In this paper, we propose a multi-objective optimization algorithm for static obstacle avoidance to improve the comfort, safety and anti-deviation of the planned trajectory.We decouple the lateral and longitudinal motion of the vehicle using the Frenet frame and discretize the driving state space to generate target states of the vehicle.Based on the initial and target states, we generate a set of lateral and longitudinal motion trajectories using quintic and quartic polynomials, respectively.In addition, we design a cost function that comprehensively considers the comfort, safety, and deviation distance of the road center line by combining acceleration check, curvature check, and collision check.As part of the cost function, we propose a novel method to quantify the safety of candidate trajectories considering the size of obstacles.The experimental results show that the proposed algorithm can quantize the safety of candidate paths and improve comfort 13.47%, 32.19%, 59.36% and 18.60% on the straight road, curvy road, intersection and U-shaped road, respectively.Furthermore, the algorithm can improve anti-deviation by 63.72%, 13.86%, 44.36%, 45.56% on the straight road, curvy road, intersection and U-shaped road, respectively.
Image captioning using transformer-based double attention network

Image captioning generates a human-like description for a query image, which has attracted considerable attention recently. The most broadly utilized model for image description is an encoder–decoder structure, where the encoder extracts the visual information of the image, and the decoder generates textual descriptions of the image. Transformers have significantly enhanced the performance of image description models. However, a single attention structure in transformers cannot consider more complex relationships between key and query vectors. Furthermore, attention weights are assigned to entire candidate vectors based on the assumption that entire vectors are related. In this paper, a new double-attention framework is presented, which improves the encoder–decoder structure to consider image captioning problems. Hence, a local generator module and a global generator module are designed to predict textual descriptions collaboratively. The proposed approach improves Self-Attention (SA) from two aspects to enhance the performance of image description. First, a Masked Self-Attention module is presented to attend on the most relevant information. Second, to evade a single shallow attention distribution and make deeper internal relations, a Hybrid Weight Distribution (HWD) module is proposed, that develops SA to use the relations between key and query vectors efficiently. Experiments over the Flickr30k and MS-COCO datasets prove that the proposed approach achieves desirable performance on different evaluation measures compared to the state-of-the-art frameworks.
QoEyes: Towards Virtual Reality Streaming QoE Estimation Entirely in the Data Plane

In recent years, advances in virtual reality (VR) technologies (e.g., high-quality VR headsets) have enabled a new perspective of experiences for users (e.g., gaming, online events). However, ensuring the user experience is still a challenge. Existing solutions are limited to measuring and estimating QoE at the user plane (e.g., VR player) or at the control plane, imposing unfeasible latency for different scenarios (5G networks and beyond). In this work, we propose QoEyes, an in-network QoE estimation based on the use of Inter-Packet-Gap (IPG) in programmable devices. Our results show that the IPG measured on the data plane is strongly linked to QoE, yielding an accurate data plane QoE estimate.
Blockchain-based IoMT for an intelligent healthcare system using a drop-offs queue

Integration of Blockchain technology and the Internet of Medical Things (IoMT) in intelligent healthcare systems to offer decentralised, trustworthy, secure, reliable, and transparent healthcare services. Transaction latency, patient waiting time, scalability, performance optimisation, device connectivity, dynamic decision-making, and patient service are increasingly crucial in developing Blockchain-integrated IoMT healthcare systems. Most previously proposed works have focused on cloud, fog and edge computing integration with Blockchain technology, where a rise in the number of devices in the Fog layer makes it more challenging to process, collect, filter, and connect devices for vulnerability detection and authentication. It is also observed that unauthorised and vulnerable activities are also rising relatively. In this research, we proposed an IoMT framework with Blockchain integration that enables Cloud, Fog, and Mist Computing environments for healthcare systems and decreases network overload, energy consumption, long-distance communication, network traffic, resource utilisation, and healthcare service time. A safe, decentralised, transparent, irreversible, and reliable platform for sharing data in the healthcare industry is being created by Blockchain and smart contracts and their integration into the Internet of Medical Things (IoMT). Moreover, a drop-off queueing mechanism is employed to connect the Mist server through an access management module to shorten service times and patient wait times while providing access control for device connections.
Privacy-Preserving Biometrics Image Encryption and Digital Signature Technique Using Arnold and ElGamal

The scientific study of privacy-preserving biometrics, represented by the palmprint, face, and iris, has grown tremendously. That being said, there has not been much attention paid to the proper preservation, transmission, and authentication of biometric images used in everyday applications. In this paper, we propose a new complete model for encrypting and decrypting biometric images, including their signing and authentication, using a nested algorithm of 3D Arnold Transform. In addition, the ElGamal Encryption Algorithm for the encryption part and the ElGamal Digital Signature for the signature part are applied. The model is mainly based on the Arnold Transform and Public-Key Cryptosystem, which are convenient for key transfer and fully functional. Here, the model succeeds in encrypting and securing the authentication process for privacy-preserving biometric images. Various tests have been carried out to demonstrate the feasibility and security of the proposed model and have been compared with existing encryption methods to achieve better results. Moreover, the proposed model can also be extended to the storage, transmission, and authentication of biometric data for daily use.
Nonlinear Program Construction and Verification Method Based on Partition Recursion and Morgan's Refinement Rules

The traditional program refinement strategy cannot be refined to an executable program, and there are issues such as low verification reliability and automation. To solve the above problems, this paper proposes a nonlinear program construction and verification method based on partition recursion and Morgan's refinement rules. First, we use recursive definition technique to characterize the initial specification. The specification is then transformed into GCL(Guarded Command Language) programs using loop invariant derivation and Morgan's refinement rules. Furthermore, VCG (Verification Condition Generator) is used in the GCL program to generate the verification condition automatically. The Isabelle theorem prover then validates the GCL program's correctness. Finally, the GCL code generates a C++ executable program automatically via the conversion system. The effectiveness of this method is demonstrated using binary tree preorder traversal program construction and verification as an example. This method addresses the problem that the construction process's loop invariant is difficult to obtain and the refinement process is insufficiently detailed. At the same time, the method improves verification process automation and reduces the manual verification workload.
Methodology for mechanical design using augmented reality as a learning tool

Augmented Reality is a tool that is being gradually developed in university institutions based on the boom caused by the use of mobile devices and Internet access from almost anywhere.