Reviewerly/exHarmony huggingface dataset:
BuggIn: Automatic Intrinsic Bugs Classification Model using NLP and ML

Recent studies have shown that bugs can be categorized into in- trinsic and extrinsic types. Intrinsic bugs can be backtracked to specific changes in the version control system (VCS), while extrin- sic bugs originate from external changes to the VCS and lack a direct bug-inducing change. Using only intrinsic bugs to train bug prediction models has been reported as beneficial to improve the performance of such models. However, there is currently no auto- mated approach to identify intrinsic bugs. To bridge this gap, our study employs Natural Language Processing (NLP) techniques to automatically identify intrinsic bugs. Specifically, we utilize two embedding techniques, seBERT and TF-IDF, applied to the title and description text of bug reports. The resulting embeddings are fed into well-established machine learning algorithms such as Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, and K-Nearest Neighbors. The primary objective of this paper is to assess the performance of various NLP and machine learning techniques in identifying intrinsic bugs using the textual informa- tion extracted from bug reports. The results demonstrate that both seBERT and TF-IDF can be effectively utilized for intrinsic bug identification. The highest performance scores were achieved by combining TF-IDF with the Decision Tree algorithm and utilizing the bug titles (yielding an F1 score of 78%). This was closely fol- lowed by seBERT, Support Vector Machine, and bug titles (with an F1 score of 77%). In summary, this paper introduces an innovative approach that automates the identification of intrinsic bugs using textual information derived from bug reports.
The Case for Scalable Quantitative Neural Network Analysis

Neural networks are an increasingly common tool for solving problems that require complex analysis and pattern matching, such as identifying stop signs in a self driving car or processing medical imagery during diagnosis. Accordingly, verification of neural networks for safety and correctness is of great importance, as mispredictions can have catastrophic results in safety critical domains. As neural networks are known to be sensitive to small changes in input, leading to vulnerabilities and adversarial attacks, analyzing the robustness of networks to small changes in input is a key piece of evaluating their safety and correctness. However, there are many real-world scenarios where the requirements of robustness are not clear cut, and it is crucial to develop measures that assess the level of robustness of a given neural network model and compare levels of robustness across different models, rather than using a binary characterization such as robust vs. not robust.
FedDefender: Backdoor Attack Defense in Federated Learning

Federated Learning (FL) is a privacy-preserving distributed machine learning technique that enables individual clients (e.g., user participants, edge devices, or organizations) to train a model on their local data in a secure environment and then share the trained model with an aggregator to build a global model collaboratively. In this work, we propose FedDefender, a defense mechanism against targeted poisoning attacks in FL by leveraging differential testing. FedDefender first applies differential testing on clients’ models using a synthetic input. Instead of comparing the output (predicted label), which is unavailable for synthetic input, FedDefender fingerprints the neuron activations of clients’ models to identify a potentially malicious client containing a backdoor. We evaluate FedDefender using MNIST and FashionMNIST datasets with 20 and 30 clients, and our results demonstrate that FedDefender effectively mitigates such attacks, reducing the attack success rate (ASR) to 10% without deteriorating the global model performance.
Towards Safe ML-Based Systems in Presence of Feedback Loops

Machine learning (ML) based software is increasingly being deployed in a myriad of socio-technical systems, such as drug monitoring, loan lending, and predictive policing. Although not commonly considered safety-critical, these systems have a potential to cause serious, long-lasting harm to users and the environment due to their close proximity and effect on the society. One type of emerging problem in these systems is unintended side effects from a feedback loop; the decision of ML-based system induces certain changes in the environment, which, in turn, generates observations that are fed back into the system for further decision-making. When this cyclic interaction between the system and the environment repeats over time, its effect may be amplified and ultimately result in an undesirable. In this position paper, we bring attention to the safety risks that are introduced by feedback loops in ML-based systems, and the challenges of identifying and addressing them. In particular, due to their gradual and long-term impact, we argue that feedback loops are difficult to detect and diagnose using existing techniques in software engineering. We propose a set of research problems in modeling, analyzing, and testing ML-based systems to identify, monitor, and mitigate the effects of an undesirable feedback loop.
Gamified Virtual Reality for Computational Thinking

In Computer Science Education, coding activities are extremely important to teach younger students the basics of programming and computational thinking. To provide an immersive experience, in this paper, we propose VRCoding, a Virtual Reality (VR)-based block coding system. VRCoding can teach computational thinking in an immersive Virtual Reality environment, exploiting passive haptics to improve interaction and give tactile feedback to the users. Passive haptics is obtained using simple physics placeholders, i.e., textured parallelepipeds, that are tracked in real-time, and aligned with the coding blocks in VR. The system is tested on a group of secondary school users, performing simple coding exercises with a standard monitor-based block coding environment and with the proposed VRCoding block language. Results show positive feedback concerning the sense of presence and the user experience.
MLGuard: Defend Your Machine Learning Model!

Machine Learning (ML) is used in critical highly regulated and high-stakes fields such as finance, medicine, and transportation. The correctness of these ML applications is important for human safety and economic benefit. Progress has been made on improving ML testing and monitoring of ML. However, these approaches do not provide i) pre/post conditions to handle uncertainty, ii) defining corrective actions based on probabilistic outcomes, or iii) continual verification during system operation. In this paper, we propose MLGuard, a new approach to specify contracts for ML applications. Our approach consists of a) an ML contract specification defining pre/post conditions, invariants, and altering behaviours, b) generated validation models to determine the probability of contract violation, and c) an ML wrapper generator to enforce the contract and respond to violations. Our work is intended to provide the overarching framework required for building ML applications and monitoring their safety.
Do Developers Fix Continuous Integration Smells?

Continuous Integration (CI) is a common software engineering practice in which the code changes are frequently merged into a software project repository after automated builds and tests have been successfully run. CI enables developers to quickly detect bugs, enhance the quality of the code, and shorten review times. However, developers may encounter some obstacles in following the CI principles. They may be unaware of them, they may follow the principles partially or they may even act against them. These behaviors result in CI smells. CI smells may in turn lessen the benefits of CI. Addressing CI smells rapidly allows software projects to fully reap the benefits of CI and increase its effectiveness. The main objective of this study is to investigate how frequently developers address CI smells. To achieve this objective, we first selected seven smells, then implemented scripts for detecting these smells automatically, and then ran the scripts in eight open-source software projects using GitHub Actions. To assess the resolution extent of CI smells by practitioners, we calculated the occurrences and time-to-resolution (TTR) of each smell. Our results suggest that Skipped Job smell has been fixed slightly more than other CI smells. The most frequently observed smell was Long Build, which was detected in an average of 19.03% of all CI builds. Fake Success smell does not get resolved in projects where it exists. Our study reveals that practitioners do not fix CI smells in practice. Further studies are needed to explore the underlying reasons behind this, in order to recommend more effective strategies for addressing these smells.
Securing Smart Cities: Unraveling Quantum as a Service

Smart Cities attract significant attention and investment from government and private entities, leading to their rapid development. With such growth in Smart Cities, data volume and diversity increase due to Internet of Things (IoT) sensors in devices. However, this abundance of data exposes millions of vulnerable devices to cyber threats, risking compromised security and sensitive information. To address these risks, Smart Cities utilize Intelligent Operations Centers (IOCs) equipped with Machine Learning (ML) algorithms. These algorithms continuously monitor and protect against security incidents. Although Quantum Computing (QC) has shown promise in Smart City applications, its usage as a service is still in the early stages. In this paper, we propose to investigate the utilization of Quantum as a Service (QaaS) to develop an architecture for securing a Smart City. Our approach employs Quantum Classifiers, QBoost from D-Wave Leap Quantum Cloud and Variational Quantum Classifier, and PegaSoS Quantum Support Vector Classifier from IBM Quantum Services. These provide real-time data classification and a user-friendly dashboard to display security incidents in the Smart City. Among the three quantum classifiers considered for the proposed architecture, QBoost performed the best both regarding quality and processing time.
Quantum Computing for Learning Analytics: An Overview of Challenges and Integration Strategies

Quantum computing has emerged as a promising technology with the potential to revolutionize various fields, including learning analytics. This research paper explores the applications of quantum computing in learning analytics and discusses the suitability of quantum techniques for addressing the challenges posed by large-scale educational datasets. It also investigates the integration of quantum computing with existing learning analytics pipelines, highlighting compatibility issues, data representation and transformation challenges, algorithmic complexity, and evaluation considerations. By understanding the potential benefits, limitations, and integration strategies, researchers can pave the way for the development of innovative tools and approaches to analyze educational data and provide personalized learning experiences.
On the Need of Quantum-Oriented Paradigm

Since the invention of Quantum Computing (QC) in the 1980s, substantial claims about QC’s ability to solve computational problems of unparalleled complexity have emerged. However, forty years later, no significant real-world QC applications exist. Indeed, the availability of small-scale noisy quantum computers is to blame. Still, simultaneously, the programming of quantum computers is too close to quantum hardware, requiring software engineers with specialized backgrounds to build QC applications and limiting the maximum exploitation of QC’s potential. Thus, there is a need for an abstract yet intuitive quantum-oriented paradigm (QOP) for building QC applications, similar to the object-oriented paradigm established in the 1960s for classical computers that laid the foundations of modern programming and modeling languages for classical computers. Unfortunately, such a QC paradigm doesn’t exist. Thus, we foresee the need to build a novel QOP based on which future quantum programming and modeling languages shall be developed. Such QOP shall enable users with diverse backgrounds (e.g., computer scientists, software engineers, and physicists) to build QC applications cost-effectively, intuitively, and independently of low-level quantum mechanics characteristics (e.g., superposition and entanglement). This paper discusses the emerging work of QOP and presents research directions that the software engineering and programming communities can follow to build a successful QOP.
Interpretable On-the-Fly Repair of Deep Neural Classifiers

Deep neural networks (DNNs) are vital in safety-critical systems but remain imperfect, leading to misclassification post-deployment. Prior works either make the model abstain from predicting in uncertain cases and so reduce its overall accuracy, or suffer from being uninterpretable. To overcome the limitations of prior work, we propose an interpretable approach to repair misclassifications after model deployment, instead of discarding them, by reducing the multi-classification problem into a simple binary classification. Our proposed technique specifically targets the predictions that the model is uncertain about them, extracts the training data that is positively and negatively incorporated into those uncertain decisions, and uses them to repair the cases where uncertainty leads to misclassification. We evaluate our approach on MNIST. The preliminary results show that our technique can repair 10.7% of the misclassifications on average, improving the performance of the models, and motivating the applicability of on-the-fly repair for more complex classifiers and different modalities.
Comparing Word-Based and AST-Based Models for Design Pattern Recognition

Design patterns (DPs) provide reusable and general solutions for frequently encountered problems. Patterns are important to maintain the structure and quality of software products, in particular in large and distributed systems like automotive software. Modern language models (like Code2Vec or Word2Vec) indicate a deep understanding of programs, which has been shown to help in such tasks as program repair or program comprehension, and therefore show promise for DPR in industrial contexts. The models are trained in a self-supervised manner, using a large unlabelled code base, which allows them to quantify such abstract concepts as programming styles, coding guidelines, and, to some extent, the semantics of programs. This study demonstrates how two language models—Code2Vec and Word2Vec, trained on two public automotive repositories, can show the separation of programs containing specific DPs. The results show that the Code2Vec and Word2Vec produce average F1-scores of 0.781 and 0.690 on open-source Java programs, showing promise for DPR in practice.
Large Scale Study of Orphan Vulnerabilities in the Software Supply Chain

The security of the software supply chain has become a critical issue in an era where the majority of software projects use open source software dependencies, exposing them to vulnerabilities in those dependencies. Awareness of this issue has led to the creation of dependency tracking tools that can identify and remediate such vulnerabilities. These tools rely on package manager metadata to identify dependencies, but open source developers often copy dependencies into their repositories manually without the use of a package manager.
Quantum Fourier Iterative Amplitude Estimation

Monte Carlo integration is a widely used numerical method for approximating integrals, which is often computationally expensive. In recent years, quantum computing has shown promise for speeding up Monte Carlo integration, and several quantum algorithms have been proposed to achieve this goal. In this paper, we present an application of Quantum Machine Learning (QML) and Grover's amplification to build a new quantum algorithm for estimating Monte Carlo integrals. Our method, which we call Quantum Fourier Iterative Amplitude Estimation (QFIAE), decomposes the target function into its Fourier series using a Parametrized Quantum Circuit (PQC), specifically a Quantum Neural Network (QNN), and then inte- grates each trigonometric component using Iterative Quantum Amplitude Estimation (IQAE). This approach builds on Fourier Quantum Monte Carlo Integration (FQMCI) method, which also decomposes the target function into its Fourier series, but QFIAE avoids the need for numerical integration of Fourier coefficients. This approach reduces the computational load while maintaining the quadratic speedup achieved by IQAE. To evaluate the performance of QFIAE, we apply it to a test function corresponding to a particle physics scattering process and compare its accuracy with other quantum integration methods and the analytic result. Our results show that QFIAE achieves comparable accuracy while being suitable for execution on real hardware. We also demonstrate how the accuracy of QFIAE improves by increasing the number of terms in the Fourier series.
Green Energy, Emissions, and Blockchain Technology

In this chapter, we investigate and review how blockchain technology can facilitate and accelerate sustainable development by adopting renewable and clean energy and reducing greenhouse gas emissions. We first review the fundamentals and unique features of blockchain technology, such as immutability and transparency, and its applications in tackling challenging problems in a wide range of industries. We then examine the potential applications of blockchain solutions in the environmental ecosystem. We review and describe potential solutions and use cases, including secure green energy trading, green energy as digital assets, efficient tracking and reporting of CO2 and air pollution reduction, efficient tracking and reporting of sustainable energy creation, and ESG compliance methodologies and strategies. Finally, we analyze in detail a use case for the energy market, the Renewable Fuels Standard (RFS) program introduced by the Environmental Protection Agency (EPA), and propose ways in which blockchain solutions can improve the program. We consider the challenges and issues with the existing system in coping with the complicated landscape of environment protection and sustainable development, study the benefits and characteristics of blockchain-based solutions, and delineate potential challenges and opportunities going forward.
A stacked architecture-based fuzzy classifier with data position transformation using fuzzy cognitive maps

The amalgamation of fuzzy model and deep learning has become one hot topic in today’s fuzzy community. However, with the model goes deeper, a pivotal aspect for performance enhancement, the interpretability of the model will deteriorate. To enhance the classification accuracy of classifiers while ensuring interpretability, we propose a stacked architecture-based fuzzy classifier named PT-SAFC. Borrowing the hierarchically stacked thought originated from deep learning, the PT-SAFC is composed by stacking two distinct fuzzy systems, implemented by fuzzy neuro-networks. Here, we propose an improved Takagi-Sugeno-Kang (TSK) model (PTFS) for data transfer by incorporating fuzzy cognitive maps (FCM). It imparts the TSK model with the data processing capability akin to deep learning models, thereby mitigating the interpretability loss arising from an increase in model depth. Furthermore, the multi-prototypes fuzzy system for decision making (MPDFS) is constructed to map data onto classes. An enhanced gradient descent method with restriction mechanism of prototype position is designed for parameter optimization. The experiments underscore PT-SAFC’s achievement of a harmonious equilibrium between interpretability and classification accuracy. And, PT-SAFC maintains an advantage in classification performance even compared to deep learning methods. Furthermore, experiments validate PT-SAFC’s capability to manipulate data distribution to augment classification efforts.
An inductive knowledge graph embedding via combination of subgraph and type information

Conventional knowledge graph representation learn the representation of entities and relations by projecting triples in the knowledge graph to a continuous vector space. The vector representation increases the precision of link prediction and the efficiency of downstream tasks. However, these methods cannot process previously unseen entities during the knowledge graph evolution. In other words, the model trained on the source knowledge graph cannot be applied to the target knowledge graph containing new unseen entities. Recently, a few subgraph-based link prediction models obtained the inductive ability, but they all neglect semantic information. In this work, we propose an inductive representation learning model TGraiL which considers not only the topological structure but also semantic information. First, distance in the subgraph is used to encode the node's topological structure. Second, the projection matrix is used to encode the entity type information. Finally, both kinds of information are fused for training to acquire the ultimate vector representation of entities. The experimental results indicate that the model's performance has been significantly improved compared to the existing baseline models, demonstrating the method's effectiveness and superiority.
A Secure Multi-Party Computation Protocol for Graph Editing Distance against Malicious Attacks

The secure computation of the graph structure is an important element in the field of secure calculation of graphs, which is important in querying data in graphs, since there are no algorithms for the graph edit distance problem that can resist attacks by malicious adversaries. In this paper, for the problem of secure computation of similarity edit distance of graphs, firstly, the encoding method applicable to the Paillier encryption algorithm is proposed, and the XOR operation scheme is proposed according to the Paillier homomorphic encryption algorithm. Then, the security algorithm under the semi-honest model is designed, which adopts the new encoding method and the XOR operation scheme. Finally, for the malicious behaviors that may be implemented by malicious participants in the semi-honest algorithm, using the hash function, a algorithm for secure computation of graph editing distance under the malicious model is designed, and the security of the algorithm is proved, and the computational complexity and the communication complexity of the algorithm are analyzed, which is more efficient compared with the existing schemes, and has practical value. The algorithm designed in this paper fills the research gap in the existing literature on the problem of graph edit distance and contributes to solving the problem.
Neural Machine Translation Research on Syntactic Information Fusion Based on the Field of Electrical Engineering

Neural machine translation has achieved good translation results, but needs further improvement in low-resource and domain-specific translation. To this end, the paper proposed to incorporate source language syntactic information into neural machine translation models. Two novel approaches, namely Contrastive Language–Image Pre-training(CLIP) and Cross-attention Fusion (CAF), were compared to a base transformer model on EN–ZH and ZH–EN pair machine translation focusing on the electrical engineering domain. In addition, an ablation study on the effect of both proposed methods was presented. Among them, the CLIP pre-training method improved significantly compared with the baseline system, and the BLEU values in the EN–ZH and ZH–EN tasks increased by 3.37 and 3.18 percentage points, respectively.