Reviewerly/exHarmony huggingface dataset:
A Practical Approach For Workload-Aware Data Movement in Disaggregated Memory Systems

Memory disaggregation is a solid alternative to traditional server systems that can overcome memory scalability issues in next-generation HPC data centers. In a rack-level disaggregated system, multiple compute nodes with small local memory rely on remote memory pools (memory nodes) to fulfill their memory demands. An in-network memory manager manages remote memory address space and allocates it to compute nodes which can access the memory at cache-line granularity using coherent interconnects such as CXL (or GenZ). However, the memory access cost is significantly increased due to the presence of the network. Even though a page migration system can exploit the locality of memory accesses, accessing a remote page starves the block-level requests. Further, page migrations introduce additional overheads which combined with starvation may even degrade the performance. All these issues require systematic evaluation of disaggregated memory systems to achieve improved designs. This paper presents a hardware mechanism for workload-aware data movement between compute and memory pools that significantly reduces the memory access cost. Firstly, our design enables centralized hot-page migration in a multi-tiered disaggregated memory that is aware of access patterns for individual compute nodes. Secondly, we analyze the complexities of accessing a remote memory page and propose a novel solution to eliminate starvation by serving all the remote memory requests at cache block granularity and by sharing bandwidth between page and block memory requests. Lastly, we add extra hardware support to get rid of additional overheads in a page migration system. We evaluate our designs over a variety of multi-threaded benchmarks using a cycle-level simulator which is specially designed to simulate a disaggregated memory system. Our design performs 10% to 100% better than traditional RDMA-based disaggregated systems that access remote memory at page granularity and 5% to 35% better than baseline disaggregated systems that use coherent interconnects for block-level access.
Innovating Vending Machine Through Object Detection for Currency Recognition and Enhanced Cash Handling

Advancements in technology have transformed vending machines into active and autonomous devices. In recent years, vending machines have become essential as a standalone and low-maintenance system because malls and stores are not open 24 hours a day. However, in countries such as Pakistan, widespread adoption of vending machines is limited due to a lack of local manufacturing support. This study introduces a cutting-edge vending machine in order to establish an affordable, pioneering embedded system. This research presents a bill acceptor that employs a transfer learning model of object detection (FOMO) for currency recognition and authentication through an ESP 32-CAM within a mechanical cash transit system. The commercial bill acceptor, which is not readily available in Pakistan, can be replaced by the proposed bill acceptor. By offering a bill acceptor as an initial approach, which has a 71% accuracy rate through edge impulse testing and realtime trials, this study seeks to revolutionize the vending sector. This prototype is the first step towards overcoming a fundamental obstacle in local manufacturing of vending machines.
Replicability Study: Corpora For Understanding Simulink Models &amp; Projects

Background: Empirical studies on widely used model-based development tools such as MATLAB/Simulink are limited despite the tools' importance in various industries. Aims: The aim of this paper is to investigate the reproducibility of previous empirical studies that used Simulink model corpora and to evaluate the generalizability of their results to a newer and larger corpus, including a comparison with proprietary models. Method: The study reviews methodologies and data sources employed in prior Simulink model studies and replicates the previous analysis using SLNET. In addition, we propose a heuristic for determining code-generating Simulink models and assess the open-source models' similarity to proprietary models. Results: Our analysis of SLNET confirms and contradicts earlier findings and highlights its potential as a valuable resource for model-based development research. We found that open-source Simulink models follow good modeling practices and contain models comparable in size and properties to proprietary models. We also collected and distribute 208 git repositories with over 9k commits, facilitating studies on model evolution. Conclusions: The replication study offers actionable insights and lessons learned from the reproduction process, including valuable information on the generalizability of research findings based on earlier open-source corpora to the newer and larger SLNET corpus. The study sheds light on noteworthy attributes of SLNET, which is self-contained and redistributable.
HOBAT: Batch Verification for Homogeneous Structural Neural Networks

The rapid development of deep learning has significantly transformed the ecology of the software engineering field. As new data continues to grow and evolve at an explosive rate, the challenge of iteratively updating software built on neural networks has become a critical issue. While the continuous learning paradigm enables networks to incorporate new data and update accordingly without losing previous memories, resulting in a batch of new networks as candidates for software updating, these approaches merely select from these networks by empirically testing their accuracy; they lack formal guarantees for such a batch of networks, especially in the presence of adversarial samples. Existing verification techniques, based on constraint solving, interval propagation, and linear approximation, provide formal guarantees but are designed to verify the properties of individual networks rather than a batch of networks. To address this issue, we analyze the batch verification problem corresponding to several non-traditional machine learning paradigms and further propose a framework named HOBAT (BATch verification for HOmogeneous structural neural networks) to enhance batch verification under reasonable assumptions about the representation of homogeneous structure neural networks, increasing scalability in practical applications. Our method involves abstracting the neurons at the same position in a batch of networks into a single neuron, followed by an iterative refinement process on the abstracted neuron to restore the precision until the desired properties for verification are met. Our method is orthogonal to boundary propagation verification on a single neural network. To assess our methodology, we integrate it with boundary propagation verification and observe significant improvements compared to the vanilla approach. Our experiments demonstrate the enormous potential for verifying large batches of networks in the era of big data.
Dynamic Blockchain Reconfiguration: Balancing the Trilemma Trade-off Using Digital Twins

The trilemma trade-off problem between decentralisation, scalability, and security states that in blockchain systems the above properties are negatively correlated. Infrastructure, node configuration, choice of Consensus Protocol, and complexity of the underlying application are cited among the factors that affect the balance of the trade-off. Given that Blockchains are complex, dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This work proposes the use of Digital Twins as the means of optimising the trilemma trade-off of blockchain i.e., re-configuring system parameters such as to maximise scalability, decentralisation and security. Specifically, through a bi-directional feedback loop between the system and the digital twin, simulation, what-if analysis and machine learning techniques will be employed for the computation of an optimal configuration given the current system state. Furthermore, a dynamic update mechanism is proposed to allow for blockchain reconfiguration without violating the decentralisation of the system.
Fourier and the Early Days of Sound Analysis [DSP History]

Joseph Fourier’s methods (and their variants) are omnipresent in audio signal processing. However, it turns out that the underlying ideas took some time to penetrate the field of sound analysis and that different paths were first followed in the period immediately following Fourier’s pioneering work, with or without reference to him. This illustrates the interplay between mathematics and physics as well as the key role played by instrumentation, with notable inventions by outsiders to academia, such as Rudolph Koenig and Édouard-Léon Scott de Martinville.
NebulOuS: A Meta-Operating System with Cloud Continuum Brokerage Capabilities

Cloud Continuum is the paradigm that unifies and exploits resources from far edge to public and private cloud offerings, as well as processing nodes with significant capacity in between. Nowadays, the combination of all these resources for augmenting modern hyper-distributed applications becomes a necessity, especially considering the vast volumes of data, their velocity, and their variety, which constitute well known challenges of Big Data processing. In this paper, we address the main research question on how a Cloud Continuum management platform should be structured to cope with the constantly increasing challenges and opportunities of the domain. We introduce the NebulOuS architecture vision towards accomplishing substantial research contributions in the realms of Cloud Continuum brokerage. We propose an advanced architecture that enables secure and optimal application provisioning, as well as reconfiguration over the Cloud Continuum. NebulOuS introduces a novel Meta-Operating System and platform, that is currently being developed, for enabling transient Cloud Continuum brokerage ecosystems that seamlessly exploit edge and fog nodes, in conjunction with multi-cloud resources, to cope with the requirements posed by low latency applications.
The Effect of Sprint Duration to the Velocity in a Large-Scale Embedded Software Project

Background: Since there are not many empirical studies on the effect of sprint duration on efficiency and productivity in large-scale agile software developments, we collected and analyzed data from large-scale embedded software development. Aim: Our objective is to analyze the effect of sprint duration on the velocity in scrum-based software development projects. Method: We selected five scrums with the least changes from a large-scale embedded software development project of a big company and measured the change in velocity when operating with 3-week and 2-week sprint durations under the same condition. Results: Our analysis revealed that the 2-week sprint duration had about 30 percent higher development velocity. The average velocity was analyzed in four milestones over about two and half years of period, and the one-tailed T-test with an assumption of unequal variances proved that the development cycle of a 2-week sprint duration was higher than that of a 3-week sprint duration. Conclusions: In this respect, this paper contributes to improving the productivity in large-scale embedded software projects.
Blockchain-Enabled Renewable Energy Traceability with a Crypto-based Arbitrage Pricing Model

The need for Renewable Energy (RE) market decentralization and its rapid growth has led to new challenges related to energy traceability and pricing. While RE has undergone remarkable growth, the traditional methods of tracking renewable energy transactions lack transparency, making it difficult to ensure the authenticity of claims related to the source and quality of energy. Blockchain has been seen as a remarkable future technology capable of being integrated across many systems on the internet. This paper proposes a blockchain-enabled solution to address these challenges by providing a secure and transparent traceability and pricing model for RE. The proposed approach uses blockchain technology to record and verify all energy transactions in a decentralized and tamper-proof manner. Additionally, the approach suggests the incorporation of smart contracts and Crypto-based Arbitrage techniques to automate the pricing of RE and the exploitation of a fair and efficient pricing mechanism. The paper goes the extra mile and presents an RE-based hypothetical case scenario that illustrates the implementation of the proposed model in an RE market while highlighting the benefits of using blockchain technology for energy traceability and pricing. The culminating discussions show that the blockchain-enabled RE traceability and pricing model offers a secure, transparent, and efficient pricing solution that can enhance trust in the RE market while promoting the transition to a sustainable energy future.
Federated Learning for Early Dropout Prediction on Healthy Ageing Applications

The provision of social care applications is crucial for elderly people to improve their quality of life and enables operators to provide early interventions. Accurate predictions of user dropouts in healthy ageing applications are essential since they are directly related to individual health statuses. Machine Learning (ML) algorithms have enabled highly accurate predictions, outperforming traditional statistical methods that struggle to cope with individual patterns. However, ML requires a substantial amount of data for training, which is challenging due to the presence of personal identifiable information (PII) and the fragmentation posed by regulations. In this paper, we present a federated machine learning (FML) approach that minimizes privacy concerns and enables distributed training, without transferring individual data. We employ collaborative training by considering individuals and organizations under FML, which models both cross-device and cross-silo learning scenarios. Our approach is evaluated on a real-world dataset with non-independent and identically distributed (non-iid) data among clients, class imbalance and label ambiguity. Our results show that data selection and class imbalance handling techniques significantly improve the predictive accuracy of models trained under FML, demonstrating comparable or superior predictive performance than traditional ML models.
Model-Agnostic Federated Learning for Privacy-Preserving Systems

This study presents an innovative aggregation scheme for model-agnostic, local, heterogeneous data models within the domain of Federated Learning. The proposed approach imposes minimal constraints on local models, only necessitating local model parameters and distances from local data centroids for a particular query. These requirements facilitate the design of privacy-preserving learning systems. We introduce a system architecture based on federated interpolation to operationalize the proposed scheme. The accuracy of our proposed scheme is evaluated using two distinct real-world datasets. We compare our results to the extreme case of a single-client scenario having complete access to all data points. Our findings indicate that, on average, federated interpolation maintains robust accuracy, experiencing a slight reduction of less than 10% compared to the single-client model with full data access.
Exploring Technical Debt in Security Questions on Stack Overflow

Background: Software security is crucial to ensure that the users are protected from undesirable consequences such as malware attacks which can result in loss of data and, subsequently, financial loss. Technical Debt (TD) is a metaphor incurred by suboptimal decisions resulting in long-term consequences such as increased defects and vulnerabilities if not managed. Although previous studies have studied the relationship between security and TD, examining their intersection in developers' discussion on Stack Overflow (SO) is still unexplored. Aims: This study investigates the characteristics of security-related TD questions on SO. More specifically, we explore the prevalence of TD in security-related queries, identify the security tags most prone to TD, and investigate which user groups are more aware of TD. Method: We mined 117,233 security-related questions on SO and used a deep-learning approach to identify 45,078 security-related TD questions. Subsequently, we conducted quantitative and qualitative analyses of the collected security-related TD questions, including sentiment analysis. Results: Our analysis revealed that 38% of the security questions on SO are security-related TD questions. The most recurrent tags among the security-related TD questions emerged as “security” and “encryption.” The latter typically have a neutral sentiment, are lengthier, and are posed by users with higher reputation scores. Conclusions: Our findings reveal that developers implicitly discuss TD, suggesting developers have a potential knowledge gap regarding the TD metaphor in the security domain. Moreover, we identified the most common security topics mentioned in TD-related posts, providing valuable insights for developers and researchers to assist developers in prioritizing security concerns in order to minimize TD and enhance software security.
Pluggable Type Inference for Free

A pluggable type system extends a host programming language with type qualifiers. It lets programmers write types like unsigned int, secret string, and nonnull object. Typechecking with pluggable types detects and prevents more errors than the host type system. However, programmers must write type qualifiers; this is the biggest obstacle to use of pluggable types in practice. Type inference can solve this problem. Traditional approaches to type inference are type-system-specific: for each new pluggable type system, the type inference algorithm must be extended to build and then solve a system of constraints corresponding to the rules of the underlying type system. We propose a novel type inference algorithm that can infer type qualifiers for any pluggable type system with little to no new type-system-specific code-that is, “for free”. The key insight is that extant practical pluggable type systems are flow-sensitive and therefore already implement local type inference. Using this insight, we can derive a global inference algorithm by re-using existing implementations of local inference. Our algorithm runs iteratively in rounds. Each round uses the results of local type inference to produce summaries (specifications) for procedures and fields. These summaries enable improved inference throughout the program in subsequent rounds. The algorithm terminates when the inferred summaries reach a fixed point. In practice, many pluggable type systems are built on frameworks. By implementing our algorithm once, at the framework level, it can be reused by any typechecker built using that frame-work. Using that insight, we have implemented our algorithm for the open-source Checker Framework project, which is widely used in industry and on which dozens of specialized pluggable typecheckers have been built. In experiments with 11 distinct pluggable type systems and 12 projects, our algorithm reduced, by 45 % on average, the number of warnings that developers must resolve by writing annotations.
TransDPR: Design Pattern Recognition Using Programming Language Models

Current Design Pattern Recognition (DPR) methods have limitations, such as the reliance on semantic information, limited recognition of novel or modified pattern versions, and other factors. We present an introductory DPR technique by using a Programming Language Model (PLM) called TransDPR, which utilizes a Facebook pre-trained model (TransCoder), which is a Cross-lingual programming Language Model (XLM) based on a transformer architecture. We leverage an n-dimensional vector representation of programs and apply logistic regression to learn design patterns (DPs). Our approach utilizes the GitHub repository to collect singleton and prototype DP programs written in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C$</tex> ++ source code. Our results indicate that TransDPR achieves 90% accuracy and an F1-score of 0.88 on open-source projects. We evaluate the proposed model on two developed modules from Volvo Cars and invite the original developers to validate the prediction results.
Divide and Conquer the EmpiRE: A Community-Maintainable Knowledge Graph of Empirical Research in Requirements Engineering

[Background.] Empirical research in requirements engineering (RE) is a constantly evolving topic, with a growing number of publications. Several papers address this topic using literature reviews to provide a snapshot of its “current” state and evolution. However, these papers have never built on or updated earlier ones, resulting in overlap and redundancy. The underlying problem is the unavailability of data from earlier works. Researchers need technical infrastructures to conduct sustainable literature reviews. [Aims.] We examine the use of the Open Research Knowledge Graph (ORKG) as such an infrastructure to build and publish an initial Knowledge Graph of Empirical research in RE (KG-EmpiRE) whose data is openly available. Our long-term goal is to continuously maintain KG-EmpiRE with the research community to synthesize a comprehensive, up-to-date, and long-term available overview of the state and evolution of empirical research in RE. [Method.] We conduct a literature review using the ORKG to build and publish KG-EmpiRE which we evaluate against competency questions derived from a published vision of empirical research in software (requirements) engineering for 2020–2025. [Results.] From 570 papers of the IEEE International Requirements Engineering Conference (2000–2022), we extract and analyze data on the reported empirical research and answer 16 out of 77 competency questions. These answers show a positive development towards the vision, but also the need for future improvements. [Conclusions.] The ORKG is a ready-to-use and advanced infrastructure to organize data from literature reviews as knowledge graphs. The resulting knowledge graphs make the data openly available and maintainable by research communities, enabling sustainable literature reviews.
BugMiner: Automating Precise Bug Dataset Construction by Code Evolution History Mining

Bugs and their fixes in the code evolution histories are important assets for many software engineering tasks such as deriving new state-of-the-art automatic bug fixing techniques. Existing bug datasets are either manually built which is difficult to grow efficiently to a scale large enough for massive data analysis, or lack of precise information of how bugs are introduced and fixed which is critical for in-depth analysis such as buggy/fixing code identification. Moreover, the types of the bugs are typically missing in the existing bug datasets, limiting the possibility of developing high-precision type-specific approaches for enterprise-level purposes. In this work, we propose BugMiner, an approach to automatically collecting bugs from code repositories by isolating the critical changes of the bugs. We also propose a learning-based approach for automating bug type classification with relatively small manual labels of bug types. We evaluate our approach regarding the precision of bug information and the efficiency of the bug-mining process with 2,082 bugs automatically mined from 100 open-source projects. We demonstrate the improved effectiveness and efficiency in bug-fixing location identification, compared to the SOTA BugBuilder, and high recall and precision in bug-inducing location identification. We also compare our learning-based bug classification approach to traditional baseline method, indicating about 17 % improvement in classification effectiveness under macro-F1.