In a Socratic dialogue, a teacher engages a student in a series of questions to prompt critical thinking, guiding the student to clarify and elaborate on their own view and to consider alternative perspectives. In this experience report, we analyse the suitability of large language models (LLMs) as automated Socratic tutors. Postgraduate computer science students from two universities and multiple courses held one-to-one interactive dialogues with LLMs on professional ethics and technology ethics scenarios. We found that the LLMs' questions were diverse and broadly scaffolded critical thinking. Students were able to demonstrate most of the cognitive levels of Bloom's Taxonomy in their answers. However, we also observed some significant limitations of LLMs as Socratic tutors, including unfounded praise of poor responses and reformulating answers for students rather than guiding students to do this themselves. Further, although many students interacted effectively with the LLM, others did not engage authentically with the process. These limitations can potentially be mitigated via prompt engineering and activity framing. Our work contributes an LLM-supported learning activity and our lessons learned on using current state-of-the-art LLMs as Socratic tutors for teaching ethics in computing. In addition, our codebooks provide a set of criteria to drive the evaluation of LLMs used for similar activities.

Complex urban systems can be difficult to monitor, diagnose and manage because the complete states of such systems are only partially observable with sensors. State estimation techniques can be used to determine the underlying dynamic behavior of such complex systems with their highly non-linear processes and external time-variant influences. States can be estimated by clustering observed sensor readings. However, clustering performance degrades as the number of sensors and readings (i.e. feature dimension) increases. To address this problem, we propose a framework that learns a feature-centric lower dimensional representation of data for clustering to support analysis of system dynamics. We propose Unsupervised Feature Attention with Compact Representation (UFACR) to rank features contributing to a cluster assignment. These weighted features are then used to learn a reduced-dimension temporal representation of the data with a deep-learning model. The resulting low-dimensional representation can be effectively clustered into states. UFACR is evaluated on real-world and synthetic wastewater treatment plant data sets, and feature ranking outcomes were validated by Wastewater treatment domain experts. Our quantitative and qualitative experimental analyses demonstrate the effectiveness of UFACR for uncovering system dynamics in an automated and unsupervised manner to offer guidance to wastewater engineers to enhance industrial productivity and treatment efficiency.

Deep learning is being widely utilized in industrial process monitoring, control, and optimization. However, in the wastewater industry, its applications are still underexplored. This is because deep learning requires a large amount of labeled training data to induce effective predictive models. Owing to the high cost of sensors and frequency and delay in sampling and laboratory analytics, wastewater treatment process data can be sparse with varying frequencies. One option to address training data limitations is to use transfer learning. However, owing to the large covariate shift between the commonly adopted source domains for transfer learning and the target domain of wastewater processes, this approach leads to unacceptable performance. We address this issue by proposing a novel synthetic data generation method for deep predictive modeling of wastewater plants. Employing a Markov process that utilizes random walk, our technique enables the generation of abundant annotated data for our target domain. The method preserves the temporal dynamics and distribution of the original data, thereby closely mimicking the potential original samples of the domain. We extensively evaluate our method over two different high-rate-algae-based treatment datasets, demonstrating considerable performance gains over existing transfer learning. Our proposed algorithm can assist plant operators to deploy responsive supportive models with limited data.

Smart metering systems (SMS) are popular in industrial and residential areas but can risk privacy by revealing user behaviors. Homomorphic encryption (HE) is a technique that protects data privacy by enabling calculations on encrypted data. However, the high computational costs of HE can hinder real-time or resource-constrained applications. Our paper presents a framework to encrypt only selected SMS data for improved efficiency without compromising privacy significantly. By encrypting data blocks with higher entropy values, we can mitigate the leakage of key information to adversaries who may conduct privacy attacks, such as membership inference attacks (MIA). We evaluate our framework using two real-world datasets (i.e., electricity and water) to assess privacy and performance trade-offs. The results indicate a 47% performance increase while still providing a sufficient level of privacy when adopting an encryption ratio of 0.4. They demonstrate the effectiveness of the proposed framework, considering the trade-off between privacy and performance, where the user can determine the appropriate security level for privacy protection and enhance the performance using HE in practical SMS settings.

Buildings can achieve energy-efficiency by using solar passive design, energy-efficient structures and materials, or by optimizing their operational energy use. In each of these areas, efficiency can be improved if the physical properties of the building along with its dynamic behavior can be captured using low-cost embedded sensor devices. This opens up a new challenge of installing and maintaining the sensor devices for different types of buildings. In this article, we propose BuildSense, a sensing framework for fine-grained, long-term monitoring of buildings using a mix of physical and virtual sensors. It not only reduces the deployment and management cost of sensors but can also guarantee accurate and fault-tolerant data coverage for long-term use. We evaluate BuildSense using sensor measurements from two rammed-earth houses that were custom-designed for a challenging hot-arid climate so almost no artificial heating or cooling is required. We demonstrate that BuildSense can significantly reduce the cost of permanent physical sensors while still achieving fit-for-purpose accuracy, fault-tolerance, and stability. Overall, we were able to reduce the cost of a building sensor network by 60% to 80% by replacing physical sensors with virtual ones while still maintaining accuracy of ≤1.0°C and fault-tolerance of two or more predictors per virtual sensor.

Programmable logic controllers (PLCs) are computers that are hardened for industrial environments and have I/O that are used to monitor and control a physical process. Learning automata specifications from PLCs provides an interface to verification tools that use an automata language, such as Uppaal. This paper introduces the time delay Mealy machine and demonstrates that it is sufficiently expressive to model PLC software. Using the LearnLib library, we implement a custom learning method to learn models from several industrial examples and analyze the efficiency. We show that the method is able to learn from simple PLC software, but the time required to learn increases rapidly with the scale of the software.

ABSTRACT Wireless sensor networks for environmental monitoring and agricultural applications often face Long‐range requirements at low bit rates together with a large numbers of nodes. This paper presents the design and test of a novel wireless sensor network that combines a large radio range with very low power consumption and cost. Our asymmetric sensor network uses ultra‐low‐cost 40‐MHz transmitters and a sensitive software‐defined radio receiver with multi‐channel capability. Experimental radio range measurements in two different outdoor environments demonstrate a single‐hop range of up to 1.8 km. A theoretical model for radio propagation at 40 MHz in outdoor environments is proposed and validated with the experimental measurements. The reliability and fidelity of network communication over longer periods is evaluated with a deployment for distributed temperature measurements. Our results demonstrate the feasibility of the transmit‐only low‐frequency system design approach for future environmental sensor networks. Although there have been several papers proposing the theoretical benefits of this approach, to the best of our knowledge, this is the first paper to provide experimental validation of such claims. Copyright © 2011 John Wiley & Sons, Ltd.

The IEEE 802.15.4 standard specifies the most recommended medium access control (MAC) protocol for Wireless Sensor Networks (WSNs). The contention-based access scheme of the MAC is driven by a Carrier Sense Multiple Access with Collision Avoidance (CSMA-CA) algorithm. In this paper, we evaluate the performance of the CSMA-CA algorithm for indoor WSNs by using an experimental testbed. We set up trials to test the default parameters of the IEEE 802.15.4 for saturated and unsaturated conditions of arriving packets. In particular, we demonstrate that the experimental results accurately match the predictions of an analytical model for the CSMA-CA algorithm, though the latter assumes ideal transmission over the wireless medium. Finally, we show that data delivery ratio and throughput could be improved by opting during the contention for different window sizes compared to the default values of the standard specification.

Many attributes of program quality can be measured automatically. UWA Java Tools is a collection of automatic software measurement tools configured for novice Java programmers. Measurement tools are used in industry to increase productivity and improve software quality. Our research demonstrates how such tools can benefit teaching and learning in programming courses.

Wireless sensor networks for environmental monitoring and agricultural applications often face long-range requirements at low bit-rates together with large numbers of nodes. Most existing wireless sensor networks use nodes with a short-range radio and use software protocols to schedule the nodes and co-ordinate multi-hop routing in order to minimise power usage. Nodes have a relatively small radio range up to a few hundred meters that is also affected by the crop growth stage and vegetation density [2]. Disadvantages of this approach include the complexity of software protocols required to co-ordinate the nodes' behaviour and the unreliable nature of the short range radio links that requires sophisticated fault tolerance mechanisms. An alternative design solution is to use low frequency, long-range radios to build a single-hop network of a base station with satellite sensing nodes [4]. This approach has the benefits of much simpler software protocols, and that transmissions in the 40 MHz band are expected to be more reliable in outdoor settings than short range transmission in the 433 MHz or 915 MHz band used by most existing sensor network radios.

FlexiTP is a novel TDMA protocol that offers a synchronized and loose slot structure. Nodes in the network can build, modify, or extend their scheduled number of slots during execution, based on their local information. Nodes wake up for their scheduled slots; otherwise, they switch into power-saving sleep mode. This flexible schedule allows FlexiTP to be strongly fault tolerant and highly energy efficient. FlexiTP is scalable for a large number of nodes because its depth-first-search schedule minimizes buffering, and it allows communication slots to be reused by nodes outside each other's interference range. Hence, the overall scheme of FlexiTP provides end-to-end guarantees on data delivery (throughput, fair access, and robust self-healing) while also respecting the severe energy and memory constraints of wireless sensor networks. Simulations in ns-2 show that FlexiTP ensures energy efficiency and is robust to network dynamics (faults such as dropped packets and nodes joining or leaving the network) under various network configurations (network topology and network density), providing an efficient solution for data-gathering applications. Furthermore, under high contention, FlexiTP outperforms 2-MAC in terms of energy efficiency and network performance.

In this paper, we propose a fault-tolerant node scheduling scheme to solve the coverage problem in sensor networks.

A novel autonomic communication framework is proposed for two-tiered heterogeneous sensor networks. The framework consists of an autonomic data-link protocol, LADD, and a communication scheduler. Networks consist of both sensing nodes and more powerful relaying nodes. LADD is able to adapt to time varying and asymmetric wireless links with minimal energy consumption. The distributed scheduling scheme provides fair and efficient co-ordination of communication streams over multiple radio hops.

The use of wireless sensor networks for gathering environmental and safety-critical data in real time is increasing at a rapid rate. Some of the main criteria in designing sensor network architectures are energy-efficiency, self-management and self-healing. However, most protocols for data gathering and routing in sensor networks implicitly assume a regular rate of data gathering by individual nodes. While this is sufficient for sensing parameters that change slowly over time, individual nodes in a small part of a network may need to increase the rate of data gathering considerably for reporting important data in real-time. Though CSMA protocols can report increasing amount of data in theory, increased contention for the wireless medium in a small part of the network usually results in increased message collision and retransmission. In this paper, we present a novel TDMA protocol for transferring time slots from one part of the network to another part to support non-uniform and reactive sensing in different parts of a network. We discuss the design of this protocol and show that it performs much better compared to CSMA protocols both in terms of energy-efficiency and supporting nonuniform sensing.

Existing TDMA-based MAC protocols for wireless sensor networks are not specifically built to consider the harsh conditions of industrial environments where the communication channel is prone to signal fading. We propose a TDMA-based MAC protocol for wireless sensor networks built for industrial applications that uses link state dependent scheduling. In our approach, nodes gather samples of the channel quality and generate prediction sets from the sample sets in independent slots. Using the prediction sets, nodes only wake up to transmit/receive during scheduled slots that are predicted to be clear and sleep during scheduled slots that may potentially cause a transmitted signal to fade. We simulate our proposed protocol and compare its performance with a general non-link state dependent TDMA protocol and a CSMA protocol. We found that our protocol significantly improves packet throughput as compared to both the general non-link state dependent TDMA protocol and CSMA protocol. We also found that in conditions which are not perfect under our assumptions, the performance of our protocol degrades gracefully.

A new framework is presented for sensor network programming with situations. User requirements are expressed in terms of temporal and spatial constraints on the events observed by sensor network nodes. A novel spatial-temporal logic is introduced for this task. We also specify protocols for situation detection that can be executed on sensor nodes to meet those requirements. The feasibility of situation-based sensor network programming using our framework is illustrated by two examples: a temporal situation for recognising the occurrence of an explosion, and a spatial situation for detecting contours in a sensor node field.

Wireless sensor networks (WSNs) are large collections of resource limited nodes, densely deployed over a landscape. They gather and disseminate local data using multi-hop broadcasting. WSN design and deployment is hampered by currently limited knowledge of the performance characteristics of network nodes and protocols. Their systematic development, thus, requires a flexible simulation environment in which new models of specific node or network behaviours can be integrated easily. This paper introduces a loosely coupled, object oriented simulation environment for this task. The simulator is used to investigate the efficiency of flooding protocols in WSNs. For dense networks with noisy transmission, we show that using low transmission power maximizes time and resource efficiency and that the scalability of flooding for large networks is excellent. We demonstrate ways of improving flooding performance given specific deployment constraints.

Discovering a routing tree for gathering or disseminating streams of data is an important operation in many sensor network applications. However, protocols for tree discovery may have significant performance problems for certain configurations of a network and its application task.A Temporal Logic of Actions (TLA) specification of push and pull diffusion is introduced in order to analyse such problems. This specification can be used as a basis for implementation, for visualising protocol behaviours, for simulation experiments, and for formal verification of properties of the protocol.This short paper shows how such a specification is used to understand variations in the routing trees discovered by push and pull diffusion and the effect of their shape and size on protocol performance.

This paper introduces a new technique for testing that a distributed real-time system satisfies a formal timed automata specification. It outlines how to write test specifications in the language of Uppaal timed automata, how to translate those specifications into program code for executing the tests, and describes the results of test experiments on a distributed real-time system with limited hardware and software resources.

This paper describes a method for transforming concurrent Ada programs by way of abstractions into input for the UPPAAL model checker for the purpose of analyzing the real-time properties of programs. The method depends on being able to compute the best and worst case execution times of procedures called by the various tasks in a concurrent program. It employs abstractions of actions to simplify the control structure of a task, abstractions of complex data structures to more abstract variables and abstractions to simplify clocks. The method is illustrated on an Ada implementation of a kernel implementing ICPP scheduling. A TLA specification of a typical client user task is derived that can be interpreted as an UPPAAL timed automaton.

Abstract. A method is introduced for testing the conformance of implemented real-time systems to timed automata specifications. Uppaal timed automata are transformed into testable timed transition systems (TTTSs) using a test view. Fault hypotheses and a test generation algorithm for TTTSs are defined. Results of applying the method are presented.

The semantics of formal specification languages provide the foundation for their verification methods. Different semantics give rise to different styles of verification. Previous work on the verification of real-time, concurrent programs includes methods based on operational semantics and those for temporal semantics using timed transition systems. However, the promise of verification methods which combine these views has not previously been investigated. The main contribution of this paper is a proof of the equivalence of the operational and temporal semantics of a general specification language for real-time, concurrent programs. This link between semantics justifies formal methods which utilize both the temporal and operational view. A generic programming language is defined for real-time, concurrent programs and a structured operational semantics for the language. Its temporal semantics is defined by way of a translation into timed transition systems. We also define an operational semantics for timed transition systems and prove this equivalent to their standard temporal semantics. We then have two labelled transition system semanties for programs: one via timed transtition systems, and our structured operational semantics for programs. The final step is to prove the bisimilarity of these two labelled transition systems.