Reviewerly/exHarmony huggingface dataset:
An Integrated Approach to Package and Class Code- to-Architecture Mapping Using InMap

Reflexion Modelling is a successful method used in industry for Software Architectural Consistency Checking (SACC). However, it includes a mapping step that is manual and tedious, especially for large complex systems. Various studies have shown how to successfully automate the interactive mapping of the basic units of the software’s codebase, i.e. its classes, to its architecture modules. However, their inherent drawback is that the effort required by an architect to review the mapping recommendations produced, whether during the mapping occurs or at the end of mapping, can be considerable. Subsequent studies have attempted to reduce this effort by use of a hierarchical mapping approach. These studies have demonstrated a reduction in the review effort required by a software architect; however, the gain in effort reduction occurred at the price of a lower recall and precision than similar non-hierarchical mapping approaches. In this study, we present an integrated approach of automated code-to-architecture mapping that draws from hierarchical (package mapping) and non-hierarchical (class mapping) techniques to keep effort minimal for an architect with marginal loss in recall and precision. Using the harmonic mean of f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -scores and effort reduction, our results show that with our integrated approach, we could achieve 0.90 on average, compared to 0.87 for the other two methods.
Identifying Anti-Patterns in Distributed Systems With Heterogeneous Dependencies

In recent years, companies have shifted their software systems from monolithic architectures to distributed architectures such as microservices. It is not a silver bullet that solves growing complexity, and technical debt still accumulates during development and maintenance activities. Researchers have defined suites of anti-patterns to detect technical debt in monolithic systems, but these anti-patterns cannot be directly applied to distributed systems. This paper presents a method to identify anti-patterns in distributed systems using heterogeneous dependencies. To do this, we first identify multiple types of explicit and implicit entities and dependencies that can be extracted from distributed systems. Using these dependencies, we create unified models to represent distributed systems and study the models to identify and define new anti-patterns based on fundamental design principles. In our preliminary research, we identified and defined three new anti-patterns and revised three existing anti-patterns for two systems. We have confirmed that two of these anti-patterns are real problems that cannot be captured by existing approaches, and these results may facilitate further research into automated anti-pattern detection in distributed systems.
Evolvability of Machine Learning-based Systems: An Architectural Design Decision Framework

The increasing integration of machine learning (ML) in modern software systems has lead to new challenges as a result of the shift from human-determined behavior to data-determined behavior. One of the key relevant challenges concerns concept drift (CD), i.e., the potential performance degradation due to changes in the data distribution. CD may severely affect the quality of the provided services, being also difficult to predict and detect, as well as costly to address. In this context, we focus on the evolvability of ML-based systems and the architectural considerations in addressing this concern. In this paper, we propose a novel scenario-based framework to support, justify and underpin architectural design decisions that address evolvability concerns in ML-based systems. The applicability and relevance of our framework is outlined through an illustrative example. We envision our framework to be extended to address other quality attributes important to ML-based systems and, overall, provide architectural support for ML operations (MLOps). Finally, we outline our plan to apply it in a number of industrial case studies, evaluate it with practitioners, and iteratively refine it.
Use of Blockchain in Securing IoT systems with Resource Constrained Devices

Internet of Things (IoT) systems have started becoming a part of our daily lives in our homes, hospitals, offices, and other settings. As the use of IoT devices continues to grow, the need for effective security and privacy solutions becomes important. In situations where multiple users need to interact with an IoT device or system, it is essential to have a secure and flexible approach to controlling access.One potential solution is to use Blockchain-based technologies, such as Non-Fungible Tokens (NFTs), to provide secure and flexible access control. NFTs can serve as untamperable keys that allow access only to individuals who have been added as team members. This approach can help to prevent unauthorized access and to ensure that only authorized individuals can interact with the device. In addition, successful implementation of this approach across the IoT domain requires that the software is executable even on resource-constrained devices.In this paper, we present our approach that uses NFTs to control access to IoT devices. We demonstrate how this approach can be used to allow only authorized individuals to access the device, add new users to the device’s access list, and prevent unauthorized access. We also conduct an experimental study using ESP32 microcontrollers and Raspberry Pi to evaluate the performance of our approach. We find that the computational power required and the associated delays are within a few milliseconds, making this approach suitable for use even on resource-constrained devices. Overall, our results suggest that using NFTs for access control can provide a secure and flexible solution for IoT systems.
A Selection Model of Privacy Patterns

Privacy has become an increasingly essential quality to consider in a software system. Privacy practices should be adopted from the first stage of software design to safeguard personal data from unintentional information leakage. Privacy patterns have been investigated by academic and industry practitioners to address different privacy issues. The presence of these patterns is both helpful and challenging for the designer. On one hand, the privacy patterns are valuable as reusable design solutions to solve common privacy problems. On the other hand, the multitude of privacy patterns makes the choice of patterns as solutions difficult for the designer. In this paper, we propose a selection model that can assist architects in deciding suitable patterns for a software system. The selection is based on the regulatory entities and architectural characteristics implicit in the patterns. We evaluate the proposed selection model through case studies and interviews with practitioners. Our evaluation accesses the applicability and usefulness of the selection model in guiding the pattern selection for architectural design and understanding the rationale of different design decisions.
Development and Integration of Self-Adaptation Strategies for Robotics Software

Robots are becoming more prevalent in industry and society as a whole. Alongside this growth their application domain is also broadening. Each application brings with it a host of potential uncertainties that the robots should be able to handle at runtime. To tackle this, the doctoral thesis outlined in this paper proposes to address three main problems. First, the current ad-hoc state of robotics software which impedes its evolution. Second, the inability to imagine every possible uncertainty at design time leading to unexpected scenarios at runtime. Third, unexpected scenarios resulting from the reality gap between the simulated environments in which robots are developed versus the real world. These unexpected scenarios may cause a system to violate its requirements, especially in our case non-functional requirements. In an attempt to solve these problems, we plan to implement a variety of self-adaptation strategies. These strategies allow systems to change their composition to handle the afore-mentioned unexpected events during operation autonomously. To accomplish this we will need to reason about how best to integrate these strategies into the software of existing robots, as well as how existing information available to designers regarding the robots can best be utilized to improve the strategies. Lastly, these strategies and the process through which they are integrated will be assessed in their impact across different robotic case studies. Preliminary results from the work towards the thesis are also presented, alongside a consideration of its potential industrial impact.
HAMIL: High-Resolution Activation Maps and Interleaved Learning for Weakly Supervised Segmentation of Histopathological Images

Semantic segmentation of histopathological images is important for automatic cancer diagnosis, and it is challenged by time-consuming and labor-intensive annotation process that obtains pixel-level labels for training. To reduce annotation costs, Weakly Supervised Semantic Segmentation (WSSS) aims to segment objects by only using image or patch-level classification labels. Current WSSS methods are mostly based on Class Activation Map (CAM) that usually locates the most discriminative object part with limited segmentation accuracy. In this work, we propose a novel two-stage weakly supervised segmentation framework based on High-resolution Activation Maps and Interleaved Learning (HAMIL). First, we propose a simple yet effective Classification Network with High-resolution Activation Maps (HAM-Net) that exploits a lightweight classification head combined with Multiple Layer Fusion (MLF) of activation maps and Monte Carlo Augmentation (MCA) to obtain precise foreground regions. Second, we use dense pseudo labels generated by HAM-Net to train a better segmentation model, where three networks with the same structure are trained with interleaved learning: The agreement between two networks is used to highlight reliable pseudo labels for training the third network, and at the same time, the two networks serve as teachers for guiding the third network via knowledge distillation. Extensive experiments on two public histopathological image datasets of lung cancer demonstrated that our proposed HAMIL outperformed state-of-the-art weakly supervised and noisy label learning methods, respectively. The code is available at https://github.com/HiLab-git/HAMIL.
Managing the interaction cycle of “university - industrial enterprise” based on the use of crowdfunding principles

In modern conditions, the system of interaction between the university and representatives of the real sector of the economy is one of the key factors of competitiveness. In this regard, the development of a model of interaction “university - industrial enterprise” is especially relevant. The purpose of the study is to confirm the hypothesis that the graduate is the main source of partnerships for universities. The objectives of the study are: analysis of the current partnerships between the university and the graduate of the largest technical universities of the Russian Federation; determination of the levels of interaction between the university and representatives of the real sector of the economy (industrial enterprises); development of a “motivation ladder” for the graduate on the way to his transformation from an applicant who considers the university from the point of view of an assistant at the start of professional activity to a strategic partner - an industrial enterprise that considers the university as a source of research competencies; the development of an original model of interaction “university - industrial enterprise” based on the principles of crowdfunding and managing the grad[1]uate development cycle as a partner from the applicant to the employer . The authors developed a “motivational triad”, reflecting the predictive utility of cooperation in the long term. To confirm the practical significance of the conducted study, digital communication outlines were designed that describe key business processes and the achieved result at each level of interaction between the university and industrial enterprises, which can be integrated into the digital infrastructure of universities. The research described in this article was carried out using the methods of observation, collection, analysis and systematization of information.
The Problem of the Actual Infinity and the Path of the New European Classical Epistemology: from Genesis to the Modern Crisis

Introduction. The purpose of the paper is to show the prospects of the problem of actual infinity for understanding the ways of New European classical epistemology. The relevance of the work is due to the need to fill a gap in the history of New European epistemology, which today makes it difficult to understand the essence of its crisis. Such a lacuna was formed by the lack of grounds for the transition from the probabilistic concept of natural science, which takes into account apophaticism associated with the idea of actual infinity, to positive science, which presupposes a positive understanding of actual infinity. Methodology and sources. The author of the paper reconstructs and critically analyzes the attempts of the founders of New European science (Galileo and Descartes) to reform the ontological project of Nikolai Kuzansky due to the need for a positive understanding of the actual infinity. The author distinguishes between the reception of the problem of actual infinity in the New European philosophy and its objective state. Based on a comparative analysis, the author of the article establishes the identity of the logic of the development of the problem of actual infinity in the studies of Zeno of Elea and in New European epistemology, thereby testifying to its enduring significance. Results and discussion. The author shows that in the development of the problem of actual infinity, an objective regularity manifests itself, consisting in the fact that an unjustified assumption of the possibility of changing physical nature on the basis of the actual infinite really leads to a change in nature, but only the basis for this change is not actual, but potential infinity. This logic was reproduced in the history of New European epistemology. Thereby, mathematical natural science really turned out to be possible, but not on what its creators put in the basis (actual infinity), but on a derivative of it (potential infinity), and as a result it strengthens the position of supporters of non-classics. Conclusion. The problem of actual infinity makes it possible to understand the logic of the development of New European epistemology from its genesis to the modern crisis, and shows a deep interconnection between these two points.
Perturbative model of noisy quantum signal processing

Recent progress in quantum signal processing (QSP) and its generalization, quantum singular value transformation, has led to a grand unification of quantum algorithms. However, inherent experimental noise in quantum devices severely limits the length of realizable QSP sequences. We consider a model of QSP with generic perturbative noise in the signal processing basis and present a diagrammatic notation useful for analyzing such errors. To demonstrate our technique, we study a specific coherent error, that of under- or overrotation of the signal processing operator parametrized by $\ensuremath{\epsilon}\ensuremath{\ll}1$. For this coherent error model, it is shown that while Pauli $Z$ errors are not recoverable without additional resources, Pauli $X$ and $Y$ errors can be arbitrarily suppressed by coherently appending a noisy recovery QSP without the use of additional resources or ancillas. Furthermore, through a careful accounting of errors using our diagrammatic tools, we provide an upper and lower bound on the length of this recovery QSP operator. We anticipate that the perturbative technique and the diagrammatic notation proposed here will facilitate future study of generic noise in QSP and quantum algorithms.
Mapless navigation for UAVs via reinforcement learning from demonstrations

This paper is concerned with the problems of mapless navigation for unmanned aerial vehicles in the scenarios with limited sensor accuracy and computing capability. A novel learning-based algorithm called soft actor-critic from demonstrations (SACfD) is proposed, integrating reinforcement learning with imitation learning. Specifically, the maximum entropy reinforcement learning framework is introduced to enhance the exploration capability of the algorithm, upon which the paper explores a way to sufficiently leverage demonstration data to significantly accelerate the convergence rate while improving policy performance reliably. Further, the proposed algorithm enables an implementation of mapless navigation for unmanned aerial vehicles and experimental results show that it outperforms the existing algorithms.
Chrontext: Portable SPARQL queries over contextualised time series data in industrial settings

Industrial information models are standardised ways of representing industrial devices, equipment, and processes together with the data collected from associated sensors and control systems. Companies invest in such models to enable digitalisation and modular, reusable solutions. They also invest heavily in analytics (e.g. machine learning) based on time series data sets to improve operations. Queries that use such context to retrieve time series data can make industrial data sets more accessible to practitioners performing analytics and application development. Moreover, they can enable scalable deployment of resulting analytical models. Industrial availability constraints require that queries over context and time series should be portable in general, as they should be able retrieve data for training in a cloud setting and production data for deployment in an on-premise setting. Solving this problem is challenging with existing approaches as context and time series data tend to exist in separate, specialised databases. We address the issue by proposing a hybrid query engine, namely Chrontext, in the setting of a SPARQL database hosting the static model, and an arbitrary time series database. We show how with a set of annotations in the knowledge graph, SPARQL queries can be evaluated over such a hybrid architecture. We provide a proof showing that our approach correctly answers SPARQL 1.1. queries. We implement our approach in Rust under the Apache 2.0 license, and use the Apache Arrow-based Polars library together with configurable pushdowns to achieve high performance. We compare the performance of Chrontext against Ontop, one of the most prominent virtual knowledge graph systems, on a synthetic data set based on industrial standards. Data are stored in a S3 data lake and PostgreSQL with the Dremio data lakehouse as the SQL integrator. We find that our approach performs 10× to 85× faster and consumes much less memory than Ontop.
Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks

Collaborative machine learning settings such as federated learning can be susceptible to adversarial interference and attacks. One class of such attacks is termed model inversion attacks , characterised by the adversary reverse-engineering the model into disclosing the training data. Previous implementations of this attack typically only rely on the shared data representations, ignoring the adversarial priors, or require that specific layers are present in the target model, reducing the potential attack surface. In this work, we propose a novel context-agnostic model inversion framework that builds on the foundations of gradient-based inversion attacks, but additionally exploits the features and the style of the data controlled by an in-the-network adversary. Our technique outperforms existing gradient-based approaches both qualitatively and quantitatively across all training settings, showing particular effectiveness against the collaborative medical imaging tasks. Finally, we demonstrate that our method achieves significant success on two downstream tasks: sensitive feature inference and facial recognition spoofing.
Assessing the Potential Impact of Large Language Models on Labor Markets and Business Cycles in China: A Preliminary Study

Large language models (LLMs) are increasingly being used in various fields, including labor markets and business cycles. In this paper, we examine the impact potential of LLMs in labor markets and business cycles in China through a qualitative case study. We first provide background information on LLMs and labor markets and business cycles in China, and then review the relevant literature. Next, we describe the methodology of our study, including the data collection process and data analysis techniques. We present the findings of our case study and identify themes and patterns related to the impact potential of LLMs. We then discuss the implications of our findings for the use of LLMs in labor markets and business cycles in China, compare our findings to the literature review, and offer suggestions for future research. Our study contributes to the growing body of literature on LLMs and their impact potential in various fields and provides insights into the potential use of LLMs in labor markets and business cycles in China.
Assessing the Usability of Two Declarative Programming Languages to Model Geometric Events

When space missions plan scientific actions for robotic spacecraft to execute, they frequently do so within a geometric context called an opportunity. Although there are geometric software libraries that let users write code to search for opportunities, they require knowledge of algorithms and imperative programming languages, which is a condition that might exclude a potentially large population of scientists. Additionally, there might be more user-friendly software systems for scientists to model and search for opportunities, but those might exclude other missions due to export concerns or an inability to maintain such software due to lack of staff or funding. To address these concerns, we designed two different computer languages to model opportunities. In this paper, we present these two languages, our study to evaluate their relative readability and usability, and results obtained in our research along with an interpretation of the same. The metric for this study has been a questionnaire with active exercises, statements with corresponding responses on a Likert scale, and open-ended questions to elicit qualitative responses. The study’s quantitative results provide us with relative and absolute quantification of the usability and readability of each language, while the study’s qualitative results help us direct future language design decisions.
‘Super-Unsupervised’ Classification for Labelling Text: Online Political Hostility as an Illustration

Abstract We live in a world of text. Yet the sheer magnitude of social media data, coupled with a need to measure complex psychological constructs, has made this important source of data difficult to use. Researchers often engage in costly hand coding of thousands of texts using supervised techniques or rely on unsupervised techniques where the measurement of predefined constructs is difficult. We propose a novel approach that we call ‘super-unsupervised’ learning and demonstrate its usefulness by measuring the psychologically complex construct of online political hostility based on a large corpus of tweets. This approach accomplishes the feat by combining the best features of supervised and unsupervised learning techniques: measurements of complex psychological constructs without a single labelled data source. We first outline the approach before conducting a diverse series of tests that include: (i) face validity, (ii) convergent and discriminant validity, (iii) criterion validity, (iv) external validity, and (v) ecological validity.