Abstract

Cyber-physical systems (CPS), robotics, the Internet of Things (IoT), and automotive systems are integral to modern technology. They are characterized by their safety criticality, accuracy, and real-time control requirements. Control software plays a crucial role in achieving these objectives by managing and coordinating the operations of various sub-systems. This paper presents a novel systematic mapping study (SMS) for control software engineering, analyzing 115 peer-reviewed papers. The study identifies, classifies, and maps existing solutions, providing a comprehensive and structured overview for practitioners and researchers. Our contributions include (i) a unique classification of literature into six research themes—engineering phases, engineering approaches, engineering paradigms, engineering artefacts, target application domains, and engineering concerns; (ii) insights into the specificity of approaches to target technologies and phases; (iii) the prominence of model-driven approaches for design and testing; (iv) the lack of end-to-end engineering support in existing approaches; and (v) the emerging role of agile-based methods versus the dominance of waterfallbased methods. This paper’s significance lies in its thorough analysis and the high-level mapping of the solution space, offering new perspectives and a detailed roadmap for future research and innovation in control software engineering. The findings will guide advancements and best practices in the field, underscoring the paper’s impact.

Abstract

Lane marker identification is crucial for developing Intelligent Transportation Systems and Autonomous Vehicles. While deep learning models excel in accuracy, their high com- putational requirements make them unsuitable for low-power edge devices. While various image-processing-based algorithms are used to pre-process images at different stages, there is a lack of an efficient algorithm specifically designed to enhance the lane-modeling stage. This paper proposes a two-stage pre- processing algorithm, consisting of a static phase and a dy- namic phase, to implement a divide-and-conquer approach for enhancing existing lane-modeling algorithms. The static phase generates an initial pixel label map and seed locations, while the dynamic phase uses the pre-processed input image and seed locations to generate a dynamic pixel map for localizing lane marking zones. Focusing on the center two lanes, the proposed method improves overall accuracy and recall, which is critical for capturing smaller lane details. Experimental results demonstrate the superior performance of the proposed two-stage algorithm compared to traditional HT and PHT methods, with the two- stage algorithm outperforming the standard HT(0.88 vs 0.92) and PHT(0.89 vs 0.95) in terms of Average Accuracy and Recall. Additionally, the two-stage PHT method significantly reduces power consumption compared to the Ultra Fast Lane Detection (UFLD) model, making it ideal for low-power edge devices like the Raspberry Pi 4B and Jetson Nano.

Abstract

In the legal domain, research efforts are being made to enhance precedent retrieval by exploiting features based on meta-data, catchphrases, citations, sentences, paragraphs, etc. It is reported in the legal domain that the text surrounding a citation provides information to identify the referenced judgment and supplies additional information about the referenced judgment and its connection to the formulated argument. In this paper, we have exploited the resourcefulness of the text surrounding the citation to improve the document representation of the referenced judgment. Experiments conducted on Indian court judgments show that the proposed Preceding citation Anchor Text (PAT)-based approach captures certain nuances that are not captured by the text present in the referenced judgment, indicating that there is a scope to exploit PAT to improve the performance of precedent retrieval systems.

Abstract

Smart home technology integrates various devices and systems that upgrade the convenience, security, and efficiency of a home. Due to these benefits, this technology is gaining global attention. In this paper, we have explored the perspectives of the residents of Mumbai on smart home technology adoption. A survey of 425 respondents was conducted, over a period of 3 months, covering demographics, awareness, preferences, concerns, and willingness to adopt. The key factors influencing their willingness to adopt such technology have been identified. The findings reveal insights into the perceived benefits, barriers, and demographic influences on smart home technology adoption in Mumbai. Most respondents show a high level of willing-ness to install smart home technology. More than 40% of respondents con-sider security their top priority when thinking about smart home features. Data privacy and security are the top concerns among the respondents, as they are worried about their personal data management and device vulnerabilities. The results are valuable for policymakers, advertisers, and developers working to promote smart home technology in the urban areas of India.

Abstract

The limited generalization of coreference resolution (CR) models has been a major bottleneck in the task’s broad application. Prior work has identified annotation differences, especially for mention detection, as one of the main reasons for the generalization gap and proposed using additional annotated target domain data. Rather than relying on this additional annotation, we propose an alternative referential task, Major Entity Identification (MEI), where we: (a) assume the target entities to be specified in the input, and (b) limit the task to only the frequent entities. Through extensive experiments, we demonstrate that MEI models generalize well across domains on multiple datasets with supervised models and LLM-based few-shot prompting. Additionally, MEI fits the classification framework, which enables the use of robust and intuitive classification-based metrics. Finally, MEI is also of practical use as it allows a user to search for all mentions of a particular entity or a group of entities of interest.

Abstract

Seismic signal classification plays a crucial role in mitigating the impact of seismic events on human lives and infrastructure. Traditional methods in seismic hazard assessment often overlook the inherent uncertainties associated with the prediction of this complex geological phenomenon. This work introduces a probabilistic framework that leverages Bayesian principles to model and quantify uncertainty in seismic signal classification by applying a Bayesian Convolutional Neural Network (BCNN). The BCNN was trained on a dataset that comprises waveforms detected in the Southern California region and achieved an accuracy of 99.1%. Monte Carlo Sampling subsequently creates a 95% prediction interval for probabilities that considers epistemic and aleatoric uncertainties. The ability to visualize both aleatoric and epistemic uncertainties provides decision-makers with information to determine the reliability of seismic signal classifications. Further, the use of Bayesian CNN for seismic signal classification provides a more robust foundation for decision-making and risk assessment in earthquake-prone regions.

Abstract

This paper introduces an ultra-low-power RC oscillator that utilizes a resistance amplification technique. The resistance amplifier achieves the effect of a large resistance by amplifying the I-V characteristics of a small resistance, resulting in significant area savings. To improve precision in oscillation frequency, a continuous time auto-zeroing technique is incorporated to dynamically calibrate the offset voltage of an integrated amplifier. Fabricated using a 180−nm CMOS process, the designed oscillator produces a 100−Hz clock signal while consuming only 2 nW power under compact silicon area (0.12mm2). Additionally, it achieves a competitive temperature coefficient (TC) of 280ppm/∘C and line sensitivity (LS) of 1.1 % / V without the need for any calibration.

Abstract

Moving Target Defense (MTD) has emerged as a proactive and dynamic framework to counteract evolving cyber threats. Traditional MTD approaches often rely on assumptions about the attackers knowledge and behavior. However, real-world scenarios are inherently more complex, with adaptive attackers and limited prior knowledge of their payoffs and intentions. This paper introduces a novel approach to MTD using a Markov Decision Process (MDP) model that does not rely on predefined attacker payoffs. Our framework integrates the attackers real-time responses into the defenders MDP using a dynamic Bayesian Network. By employing a factored MDP model, we provide a comprehensive and realistic system representation. We also incorporate incremental updates to an attack response predictor as new data emerges. This ensures an adaptive and robust defense mechanism. Additionally, we consider the costs of switching configurations in MTD, integrating them into the reward structure to balance execution and defense costs. We first highlight the challenges of the problem through a theoretical negative result on regret. However, empirical evaluations demonstrate the frameworks effectiveness in scenarios marked by high uncertainty and dynamically changing attack landscapes.

Abstract

Dynamic control software reconfiguration for the Internet of Things (IoT) and cyber-physical systems (CPS) is crucial for adaptable and efficient automation. This paper presents a knowledge-driven architecture enabling dynamic device reconfiguration using the Web Ontology Language (OWL) and Terse Triple Language (TTL) formats. Key components include a capability ontology, session-type information for sequencing and concurrent operations, and an Integrated Development Environment (IDE) for automated control design. The capability ontology standardizes machine capabilities, facilitating device integration based on their capabilities, while session-type information ensures correct sequencing and synchronization of machine functions. The IDE platform supports dynamic reconfiguration by automating device selection, control strategy formulation, and system adjustments across diverse use cases. The architecture has been validated in real-world scenarios, including smart meeting rooms, warehouse automation, and energy management, showing a reduction in manual configuration time (up to 50%), development time (86% in some cases), and error rates (30%). Benchmarking results indicate faster code generation (40% improvement) and efficient component integration across different CPS environments. Challenges like computational complexity, scalability, and integration with existing systems highlight limitations. Future research will explore further optimizations and broader applicability to ensure low-latency, high-accuracy, and seamless integration in complex CPS. This work advances dynamic control software reconfiguration by providing a flexible solution that enhances CPS reliability and efficiency through a knowledgedriven approach.

Abstract

Developing control software for Cyber-Physical Systems (CPS) and industrial automation faces challenges like hardware integration, real-time responsiveness, and high reliability. Traditional environments often lack domain-specific knowledge, leading to inefficiencies. This paper introduces the Knowledge-aided Integrated Development Environment (K-IDE), which integrates domain knowledge into the software lifecycle, improving efficiency, consistency, and maintainability. K-IDE uses domain-specific languages (DSLs) and knowledge graphs to automate design and code generation. Key findings show that K-IDE significantly reduces development time and errors compared to traditional IDEs, proving effective in robotics and industrial automation. K-IDE bridges the gap between abstract designs and concrete implementations, offering a robust solution for modern control software development.