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.

Abstract

This paper explores the critical issue of enhancing cybersecurity measures for low-cost, Wi-Fi-based Unmanned Aerial Vehicles (UAVs) against Distributed Denial of Service (DDoS) attacks. In the current work, we have explored three variants of DDoS attacks, namely Transmission Control Proto col (TCP), Internet Control Message Protocol (ICMP), and TCP + ICMPflooding attacks, and developed a detection mechanism that runs on the companion computer of the UAV system. As a part of the detection mechanism, we have evaluated various machine learning, and deep learning algorithms, such as XGBoost, Isolation Forest, Long Short-Term Memory (LSTM), Bidirectional-LSTM (Bi-LSTM), LSTM with attention, Bi LSTM with attention, and Time Series Transformer (TST) in terms of various classification metrics. Our evaluation reveals that algorithms with attention mechanisms outperform their counterparts in general, and TST stands out as the most efficient model with a run time of ∼0.1 seconds. TST has demonstrated an F1 score of 0.999, 0.997, and 0.943 for TCP, ICMP, and TCP + ICMP flooding attacks respectively. In this work, we present the necessary steps required to build an on board DDoS detection mechanism. Further, we also present the ablation study to identify the best TST hyperparameters for DDoS detection, and we have also underscored the advantage of adapting learnable positional embeddings in TST for DDoS detection with an improvement in F1 score from 0.94 to 0.99.

Abstract

Community detection is the problem of identifying natural divisions in networks. Efficient parallel algorithms for identifying such divisions is critical in a number of applications, where the size of datasets have reached significant scales. This paper presents one of the most efficient multicore implementations of the Louvain algorithm, a high quality community detection method. On a server equipped with dual 16-core Intel Xeon Gold 6226R processors, our Louvain, which we term as GVE-Louvain, outperforms Vite, Grappolo, NetworKit Louvain, and cuGraph Louvain (running on NVIDIA A100 GPU) by 50x, 22x, 20x, and 5.8x faster respectively - achieving a processing rate of 560M edges/s on a 3.8B edge graph. In addition, GVE-Louvain improves performance at an average rate of 1.6x for every doubling of threads.

Abstract

PageRank is a widely used centrality measure that assesses the significance of vertices in a graph. Efficiently updating PageRank on dynamic graphs is essential for various applications due to the increasing scale of datasets. This paper introduces our Dynamic Frontier (DF) and Dynamic Frontier with Pruning (DF-P) approaches. Given a batch update comprising edge insertions and deletions, these approaches iteratively identify vertices likely to change their ranks with minimal overhead. On a server featuring a 64-core AMD EPYC-7742 processor, our approaches outperform Static and Dynamic Traversal PageRank by and respectively - on real-world dynamic graphs, and by and on large static graphs with random batch updates. Our approaches scale at a rate of for every doubling of threads.

Abstract

Community detection is the problem of identifying natural divisions in networks. Efficient parallel algorithms for identifying such divisions is critical in a number of applications, where the size of datasets have reached significant scales. This paper presents one of the most efficient implementations of the Leiden algorithm, a high quality community detection method. On a server equipped with dual 16-core Intel Xeon Gold 6226R processors, our Leiden implementation, which we term as GVE-Leiden, outperforms NetworKit Leiden and cuGraph Leiden (running on NVIDIA A100 GPU) by 8.2× and 3.0× respectively — achieving a processing rate of 403M edges/s on a 3.8B edge graph. In addition, GVE-Leiden improves performance at a rate of 1.6× for every doubling of threads.

Abstract

Automatic syllable stress detection methods typically consider syllable-level features as independent. However, as per linguistic studies, there is a dependency among the syllables within a word. In this work, we address this issue by proposing a Post-Net approach using Time-Delay Neural Networks to exploit the syllable dependency in a word for stress detection task. For this, we propose a loss function to incorporate the dependency by ensuring only one stressed syllable in a word. The proposed Post-Net leverages the existing SOTA sequence-independent stress detection models and learns in both supervised and unsupervised settings. We compare the Post-Net with three existing SOTA sequence-independent models and also with sequential model (LSTMs). Experiments conducted on ISLE corpus show the highest relative accuracy improvement of 2.1% and 20.28% with the proposed Post-Net compared to the best sequenceindependent SOTA model in supervised and unsupervised manners, respectively.

Abstract

Automatic syllable stress detection is typically operated at syllable level with stress-related acoustic features. The stress placed on a syllable is influenced not only by its own characteristics but also by its context in the word. However, traditional methods for stress detection overlook the contextual acoustic factors that influence stress placement. By addressing this issue, we study sequential modeling approaches by integrating the syllable dependency for automatic syllable stress detection using a masking strategy. This approach considers a sequence of syllables at the word level and identifies its stress label sequence. We explore various sequential models, such as RNNs, LSTMs, GRUs, and Attention networks. We conduct experiments on the ISLE corpus comprising non-native speakers speaking English. From the experiments, we observe a significant improvement in the performance with all sequential models compared to the state-of-the-art non-sequential baseline (DNN).

Abstract

There have been various attempts at training LLMs to perform deductive reasoning tasks. Some among these show that LLMs behave like humans by displaying content effect, that is, they reason better on reasoning tasks containing rules that align with our everyday beliefs and reason poorly when the rules are belief-violating. On the other hand, there are studies which challenge whether LLMs genuinely reason and attribute their reasoning to artefacts from the training data. In order to make sense of claims concerning genuine reasoning, we introduce a framework developed by Diane Proudfoot’s externalist criteria for machine cognition, which has been inspired by Wittgenstein’s thoughts on rule-following. Based on Wittgenstein's notion that deductive reasoning is normative, we propose the use of Wittgensteinian distinction between genuine rule-following and quasi rule-following as a method to distinguish genuine deductive competence from quasi-competence. In doing so, we also draw attention to the limitations and implications of Proudfoot’s claims regarding machine cognition through the introduction of a thought experiment. This thought experiment enables us to think through Proudfoot’s argument, according to which it is due to pragmatic considerations–and not in principle–that LLMs are unlikely to possess genuine reasoning.

Abstract

We develop three new methods to implement any Linear Combination of Unitaries (LCU), a powerful quantum algorithmic tool with diverse applications. While the standard LCU procedure requires several ancilla qubits and sophisticated multi-qubit controlled operations, our methods consume significantly fewer quantum resources. The first method (Single-Ancilla LCU) estimates expectation values of observables with respect to any quantum state prepared by an LCU procedure while requiring only a single ancilla qubit, and no multi-qubit controlled operations. The second approach (Analog LCU) is a simple, physically motivated, continuous-time analogue of LCU, tailored to hybrid qubit-qumode systems. The third method (Ancilla-free LCU) requires no ancilla qubit at all and is useful when we are interested in the projection of a quantum state (prepared by the LCU procedure) in some subspace of interest. We apply the first two techniques to develop new quantum algorithms for a wide range of practical problems, ranging from Hamiltonian simulation, ground state preparation and property estimation, and quantum linear systems. Remarkably, despite consuming fewer quantum resources they retain a provable quantum advantage. The third technique allows us to connect discrete and continuous-time quantum walks with their classical counterparts. It also unifies the recently developed optimal quantum spatial search algorithms in both these frameworks, and leads to the development of new ones that require fewer ancilla qubits. Overall, our results are quite generic and can be readily applied to other problems, even beyond those considered here.