Multi-Behavior recommendation captures fine-grained user intents by jointly modeling multiple interaction behaviors, significantly improving recommendation accuracy and diversity. However, existing methods often overlook complex semantic relations between items and entities. Moreover, constructing separate subgraphs for different interaction types frequently results in sparse graph structures, which exacerbates the long-tail problem. To address these challenges, we propose Long-tail Inductive Feature Transfer (LIFT), a method for knowledge-aware multi-behavior recommendation. To mitigate uneven node distribution in graph structures, we introduce a knowledge transfer-based feature reconstruction mechanism. Specifically, we first drop a portion of the neighbors of head nodes to construct proxy representations for tail nodes, training a reconstructor on the tail proxy to reconstruct the original head node features. The trained reconstructor is then used to backfill missing neighbor information for tail nodes, thereby achieving a more balanced feature distribution across nodes. Furthermore, we integrate multi-behavior and semantic contrastive learning to jointly optimize the representations. Extensive experiments on four datasets demonstrate that LIFT outperforms state-of-the-art methods, with further analysis validating its uniformity in representation learning. The source code is available at:https://github.com/city59/LIFT.

This paper proposes a novel address mapping mechanism for multi-robot communication and collaboration systems, named SPing. SPing addresses each robot with a dynamicphysical-world address- an encoding of the robot's physical location - rather than a pre-assigned digital-world ID (e.g., the IP address). This enables a “see-and-point” communication mode for robots: a robot can establish an immediate connection pointing to any other robot it intends to collaborate with in its visual field, without relying on a pre-existing multi-robot network. This on one hand improves the robustness and usefulness of multi-robot systems in uncertain and unstructured environments where network infrastructures are unavailable. On the other hand, it makes the robots' communication behavior tightly coupled with and more supportive of the collaboration tasks in the physical world. We build an end-to-end prototype of SPing and evaluate its performance in both static and mobile scenarios. The results show that SPing can always establish a connection precisely pointing to the target receiver with an average matching rate of 99.58%, and a spatial resolution of 0.3 m$\sim$0.5m.

Large Language Models (LLMs) have shown remarkable capabilities in text and multimodal processing, yet they fundamentally lack physical awareness--understanding of real-world physical phenomena. In this work, we present ACORN, a framework that teaches LLMs physical awareness through sound, focusing on fundamental physical phenomena like the Doppler effect, multipath effect, and spatial relationships. To overcome data scarcity, ACORN introduce a physics-based simulator combining real-world sound sources with controlled physical channels to generate diverse training data. Using this simulator, we build AQA-PHY, a comprehensive Audio Question-Answer dataset, and propose an audio encoder that processes both magnitude and phase information. By connecting our audio encoder to state-of-the-art LLMs, we demonstrate reasonable results in both simulated and real-world tasks, such as line-of-sight detection, Doppler effect estimation, and Direction-of-Arrival estimation, paving the way for enabling LLMs to understand physical world.

We present a novel approach to monitor the Body in White (BiW), the fundamental metallic structure of a vehicle. Existing monitoring methods, including both wired and wireless sensor systems, face significant challenges due to integration complexity, weight considerations, material costs, and signal blockage within the metallic environment. To overcome these limitations, we introduce Arach-Net, an acoustic backscatter network that leverages the conductive properties of the BiW to propagate vibration signals for energy transfer and data communication. This system comprises battery-free tags that harvest energy from BiW vibrations and utilize a backscatter technique for efficient communication, thereby eliminating the need for external power sources and reducing the power consumption. We address key challenges such as power sufficiency for tag activation and sustained operation, and collision reduction in network communication, by designing an ultra-low power backscatter tag and a distributed slot allocation protocol. We implement ArachNet, and deploy 12 tags onto the BiW of an electric SUV car. The evaluation results show that the power consumption of the tag is 51.0 μW for uplink packet transmission, and 24.8 μW for downlink packet reception. With our network protocol, the slot utilization can be up to 81.2%.

Abstract Recent studies have indicated that large language models (LLMs) remain susceptible to adversarial attacks, despite enhanced robustness through the chain-of-thought (CoT) capability. However, this capability also introduces the potential for more covert and effective adversarial attack methods. This paper proposes a CoT Transfer Adversarial attack framework (CTTA) for general LLMs. Initially, we utilize a pre-trained model based on the transformer architecture and fine-tune it on various tasks to serve as a surrogate model. Subsequently, different levels of adversarial attack algorithms are utilized, and the generated adversarial samples are used as transfer samples. A thought chain-based adversarial transfer attack framework is constructed using transfer samples and thought chain techniques. Finally, various indicators are utilized to assess the performance of the general LLMs in response to this attack. The results demonstrate that the attack framework surpasses current state-of-the-art research. Numerous experiments on LLMs with varying performance and parameter sizes have validated the effectiveness, stability, and generalizability of this attack. The model’s error response and the superiority of this attack are thoroughly examined using attention by gradient technology, confirming the security threats posed by LLMs when leveraging CoT capability. This has significant implications for enhancing the security and robustness of LLMs.

Abstract This study introduces an innovative framework named scale for processing dynamic skyline queries securely in cloud environments. Unlike previous approaches that require complex operations on encrypted data, scale simplifies dynamic skyline domination to mere comparisons, significantly improving query efficiency. Through empirical evaluations over four datasets, we show that scale accelerates query processing nearly 1000-fold compared to existing state-of-the-art methods. Specifically, scale shows significant efficiency improvements by simplifying query interactions to a single round between the user and the cloud, which is validated through empirical studies on multiple datasets. Moreover, we introduce two distributed versions of scale , dist-scale-s and dist-scale-e , which further optimize performance by facilitating parallel processing. This adaptation showcases a substantial reduction in response times and computational overhead, underpinning the scalability and effectiveness of our framework in handling large-scale, secure cloud-based queries.

mmWave-based activity recognition technology has attracted widespread attention as it provides the ability of device-free, ubiquitous and accurate sensing. Recognition of human activities intrinsically demands to be real-time and continuous, but the state of the arts is still far limited with the capacity in this regard. The main obstacle lies in activity sequence segmentation, i.e., locating the boundaries between consecutive activities in an activity sequence. This is a daunting task, due to the unclear activity boundaries and the variable activity duration. In this paper, we proposeZuMa, the first mmWave-based approach to real-time continuous activity recognition. When resorting to a machine learning model for activity recognition, our insight is that the recognition confidence of the recognition model is highly correlated to the accuracy of activity sequence segmentation, so that the former can be utilized as a feedback metric to finely adjust the segmentation boundaries. Based on this insight,ZuMais a coarse-to-fine grained approach, which includes the fast coarse-grained activity chunk extraction and the find-grained explicit segmentation adjustment and recognition. We have implementedZuMawith the commercial mmWave radar and evaluated its performance under various settings. The results demonstrate thatZuMaachieves an average recognition error of 12.67%, which is 65.08% and 71.87% lower than that of the two baseline methods. The average recognition delay ofZuMais only 1.86 s.

WiFi-based device localization is a key technology for smart applications, while most of which rely on LoS signals to work. However, in real-world indoor environments, very few LoS signals are usable for accurate localization. This article presents Bifrost , a novel hardware-software co-design to cope with this practical problem. The core idea of Bifrost is to reinvent WiFi signals to provide sufficient LoS signals. Specifically, we present a low-cost plug-in design of leaky wave antenna (LWA) that can generate orthogonal polarized signals: On the one hand, LWA disperses signals of different frequencies to different angles, thus providing AoA information for the localized target. On the other hand, the target further leverages the antenna polarization mismatch to distinguish AoAs from different LWAs. Besides, fine-grained information in CSI is exploited to mitigate multipath and noise. Besides, a dedicated Kalman filter is proposed to facilitate the cooperation of Bifrost and SpotFi, a state-of-the-art approach, to enhance the availability and accuracy of SpotFi. The evaluation results show that the median localization error of Bifrost is 0.81 m, 52.35% less than that of SpotFi. When combined with Bifrost to work in realistic settings, SpotFi can reduce the localization error by 33.54%.

Abstract Serverless computing has nowadays become a mainstream paradigm to develop cloud‐native applications owing to its high scalability, ease of usage and cost‐effectiveness. Nevertheless, because of its poor infrastructure transparency, two main challenges emerge when users migrate their applications to a serverless platform: the lack of an effective analytical model for performance and billing, and the trade‐off problem between them. In this paper, we formally define a serverless workflow and introduce the concept of execution instances. Based on them, a probabilistic performance and cost evaluation model is built to obtain their expected values for an input serverless workflow. Then, we design a tailored evolutionary optimization algorithm called EASW to tackle budget‐constrained performance optimization and performance‐constrained cost optimization problems. Extensive experiments were carried out to test the proposed model and optimization algorithm on AWS Lambda. Results reveal that our model can achieve an accuracy over 98% and EASW can yield a better memory configuration solution than existing methods for constrained optimization.

We presentMicNest: an acoustic localization system enabling precise drone landing. InMicNest, multiple microphones are deployed on a landing platform in carefully devised configurations. The drone carries a speaker transmitting purposefully-designed acoustic pulses. The drone may be localized as long as the pulses are correctly detected. Doing so is challenging:i)because of limited transmission power, propagation attenuation, background noise, and propeller interference, the Signal-to-Noise Ratio (SNR) of received pulses is intrinsically low;ii)the pulses experience non-linear Doppler distortion due to the physical drone dynamics;iii)as location information is used during landing, the processing latency must be reduced to effectively feed the flight control loop. To tackle these issues, we design a novel pulse detector, Matched Filter Tree (MFT), whose idea is to convert pulse detection to a tree search problem. We further present three practical methods to accelerate tree search jointly. Our experiments show thatMicNestcan localize a drone 120 m away with 0.53% relative localization error at 20 Hz location update frequency. For navigating drone landing,MicNestcan achieve a success rate of 94%. The average landing error (distance between landing point and target point) is only 4.3 cm.

We present Acoustic Inertial Measurement (AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In contrast, AIM exploits the acoustic characteristics of the drones to estimate their location and derive their motion, even in NLoS settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR) and demonstrate that AIM can support indoor spaces with arbitrary ranges and layouts. We implement AIM using an off-the-shelf microphone array and evaluate its performance with a commercial drone under varied settings. Results indicate that the mean localization error of AIM is 46% lower than that of commercial UWB-based systems in a complex 10m×10m indoor scenario, where state-of-the-art infrared systems would not even work because of NLoS situations. When distributed microphone arrays are deployed, the mean error can be reduced to less than 0.5m in a 20m range, and even support spaces with arbitrary ranges and layouts.

This paper proposes Meta-Speaker, an innovative speaker capable of projecting audible sources into the air with a high level of manipulability. Unlike traditional speakers that emit sound waves in all directions, Meta-Speaker can manipulate the granularity of the audible region, down to a single point, and can manipulate the location of the source. Additionally, the source projected by Meta-Speaker is a physical presence in space, allowing both humans and machines to perceive it with spatial awareness. Meta-Speaker achieves this by leveraging the fact that air is a nonlinear medium, which enables the reproduction of audible sources from ultrasounds. Meta-Speaker comprises two distributed ultrasonic arrays, each transmitting a narrow ultrasonic beam. The audible source can be reproduced at the intersection of the beams. We present a comprehensive profiling of Meta-Speaker to validate the high manipulability it offers. We prototype Meta-Speaker and demonstrate its potential through three applications: anchor-free localization with a median error of 0.13 m, location-aware communication with a throughput of 1.28 Kbps, and acoustic augmented reality where users can perceive source direction with a mean error of 9.8 degrees.

WiFi-based device localization is a key enabling technology for smart applications, which has attracted numerous research studies in the past decade. Most of the existing approaches rely on Line-of-Sight (LoS) signals to work, while a critical problem is often neglected: In the real-world indoor environments, WiFi signals are everywhere, but very few of them are usable for accurate localization. As a result, the localization accuracy in practice is far from being satisfactory. This paper presents Bifrost, a novel hardwaresoftware co-design for accurate indoor localization. The core idea of Bifrost is to reinvent WiFi signals, so as to provide sufficient LoS signals for localization. This is realized by exploiting the dispersion effect of signals emitted by the leaky wave antenna (LWA). We present a low-cost plug-in design of LWA that can generate orthogonal polarized signals: On one hand, LWA disperses signals of different frequencies to different angles, thus providing Angle-of-Arrival (AoA) information for the localized target. On the other hand, the target further leverages the antenna polarization mismatch to distinguish AoAs from different LWAs. In the software layer, fine-grained information in Channel State Information (CSI) is exploited to cope with multipath and noise. We implement Bifrost and evaluate its performance under various settings. The results show that the median localization error of Bifrost is 0.81m, which is 52.35% less than that of SpotFi, a state-of-the-art approach. SpotFi, when combined with Bifrost to work in the realistic settings, can reduce the localization error by 33.54%.

The ability to localize acoustic sources can greatly improve the perception of smart devices (e.g., a smart speaker like Amazon Alexa). In this work, we study the problem of concurrently localizing multiple acoustic sources with a single smart device. Our proposal calledSymphonyis the first complete solution to tackle the above problem, including method, theory, and practice. The method stems from the insight that the geometric layout of microphones on the array determines the unique relationship among signals from the same source along the same arriving path. We also establish the theoretical model ofSymphony, which reveals the relation between localization performance (resolution and coverage) and impacting factors (sampling rate, array aperture, and array-wall distance). Moreover, the ability to separate and localize multiple sources is also studied theoretically and numerically. We implementSymphonywith different types of commercial off-the-shelf microphone arrays and evaluate its performance under different settings. The results show thatSymphonyhas a median localization error of 0.662 m.

We present Acoustic Inertial Measurement (AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In contrast, AIM exploits the acoustic characteristics of the drones to estimate their location and derive their motion, even in NLoS settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR). We implement AIM using an off-the-shelf microphone array and evaluate its performance with a commercial drone under varied settings. Results indicate that the mean localization error of AIM is 46% lower than commercial UWB-based systems in complex indoor scenarios, where state-of-the-art infrared systems would not even work because of NLoS settings. We further demonstrate that AIM can be extended to support indoor spaces with arbitrary ranges and layouts without loss of accuracy by deploying distributed microphone arrays.

We present MicNest: an acoustic localization system enabling precise landing of aerial drones. Drone landing is a crucial step in a drone's operation, especially as high-bandwidth wireless networks, such as 5G, enable beyond-line-of-sight operation in a shared airspace and applications such as instant asset delivery with drones gain traction. In MicNest, multiple microphones are deployed on a landing platform in carefully devised configurations. The drone carries a speaker transmitting purposefully-designed acoustic pulses. The drone may be localized as long as the pulses are correctly detected. Doing so is challenging: i) because of limited transmission power, propagation attenuation, background noise, and propeller interference, the Signal-to-Noise Ratio (SNR) of received pulses is intrinsically low; ii) the pulses experience non-linear Doppler distortion due to the physical drone dynamics while airborne; iii) as location information is to be used during landing, the processing latency must be reduced to effectively feed the flight control loop. To tackle these issues, we design a novel pulse detector, Matched Filter Tree (MFT), whose idea is to convert pulse detection to a tree search problem. We further present three practical methods to accelerate tree search jointly. Our real-world experiments show that MicNest is able to localize a drone 120 m away with 0.53% relative localization error at 20 Hz location update frequency.

The database systems course in an undergraduate computer science degree program is gaining increasing importance due to the continuous supply of database-related jobs as well as the rise of Data Science. A key learning goal of learners taking such a course is to understand how SQL queries are executed in an RDBMS in practice. An RDBMS typically exposes a query execution plan (QEP) in a visual or textual format, which describes the execution steps for a given query. However, it is often daunting for a learner to comprehend these QEPs containing vendor-specific implementation details. In this demonstration, we present a novel, generic, and portable system called LANTERN that generates a natural language (NL)-based description of the execution strategy chosen by the underlying RDBMS to process a query. It provides a declarative framework called POOL for subject matter experts (SME) to efficiently create and manipulate the NL descriptions of physical operators of any RDBMS. It then exploits POOL to generate the NL descriptions of QEPs by integrating a rule-based and a deep learning-based techniques to infuse language variability in the descriptions. Such an NL generation strategy mitigates the impact of boredom on learners caused by repeated exposure of similar text generated by a rule-based system.

Recently, mmWave has been widely used in fine-grained sensing applications due to its short wavelength and large bandwidth. One mmWave device usually can measure the target's 1D micro-displacement along the line-of-sight (LOS) direction. In this work, we try to empower mmWave with the capability of measuring 2D micro-displacements. Our insight is that although the mmWave reflection from one path contains only 1D observation, the spatial separability of mmWave offers an opportunity to separate multipath reflections from the received signal. Combining the coherent observations from multipath reflections can restore the 2D orbit of the target. Based on this insight, we present GWaltz, a mmWave sensing system that manages to measure sub-mm-level 2D orbits of rotating machinery. In GWaltz, we first reveal the relationship between the rotor's movement and the observed ghost multipath reflections (GMRs) and then design a set of novel signal processing techniques to restore the rotor orbit from the poor-quality GMR signals. We implement GWaltz with a commercial mmWave radar, and our evaluation results show that it achieves an absolute error of about 8.42um when measuring 100um-diameter rotor orbits.

The database systems course is offered as part of an undergraduate computer science degree program in many major universities. A key learning goal of learners taking such a course is to understand how sql queries are processed in a rdbms in practice. Since aquery execution plan (qep ) describes the execution steps of a query, learners can acquire the understanding by perusing the qep s generated by a rdbms. Unfortunately, in practice, it is often daunting for a learner to comprehend these qep s containing vendor-specific implementation details, hindering her learning process. In this paper, we present a novel, end-to-end,generic system called lantern that generates a natural language description of a qep to facilitate understanding of the query execution steps. It takes as input an sql query and its qep, and generates a natural language description of the execution strategy deployed by the underlying rdbms. Specifically, it deploys adeclarative framework called pool that enablessubject matter experts to efficiently create and maintain natural language descriptions of physical operators used in qep s. Arule-based framework called rule-lantern is proposed that exploits pool to generate natural language descriptions of qep s. Despite the high accuracy of rule-lantern, our engagement with learners reveal that, consistent with existing psychology theories, perusing such rule-based descriptions lead toboredom due to repetitive statements across different qep s. To address this issue, we present a noveldeep learning-based language generation framework called neural -lantern that infuses language variability in the generated description by exploiting a set ofparaphrasing tools andword embedding. Our experimental study with real learners shows the effectiveness of lantern in facilitating comprehension of qep s.

Acoustic signal acts as an essential input to many systems. However, the pure acoustic signal is very difficult to extract, especially in noisy environments. Existing beamforming systems are able to extract the signal transmitted from certain directions. However, since microphones are centrally deployed, these systems have limited coverage and low spatial resolution. We overcome the above limitations and present ChordMics, a distributed beamforming system. By leveraging the spatial diversity of the distributed microphones, ChordMics is able to extract the acoustic signal from arbitrary points. To realize such a system, we further address the fundamental challenge in distributed beamforming: aligning the signals captured by distributed and unsynchronized microphones. We implement ChordMics and evaluate its performance under both LOS and NLOS scenarios. The evaluation results tell that ChordMics can deliver higher SINR than the centralized microphone array. The average performance gain is up to 15dB.

Sound recognition is an important and popular function of smart devices. The location of sound is basic information associated with the acoustic source. Apart from sound recognition, whether the acoustic sources can be localized largely affects the capability and quality of the smart device's interactive functions. In this work, we study the problem of concurrently localizing multiple acoustic sources with a smart device (e.g., a smart speaker like Amazon Alexa). The existing approaches either can only localize a single source, or require deploying a distributed network of microphone arrays to function. Our proposal called Symphony is the first approach to tackle the above problem with a single microphone array. The insight behind Symphony is that the geometric layout of microphones on the array determines the unique relationship among signals from the same source along the same arriving path, while the source's location determines the DoAs (direction-of-arrival) of signals along different arriving paths. Symphony therefore includes a geometry-based filtering module to distinguish signals from different sources along different paths and a coherence-based module to identify signals from the same source. We implement Symphony with different types of commercial off-the-shelf microphone arrays and evaluate its performance under different settings. The results show that Symphony has a median localization error of 0.694m, which is 68% less than that of the state-of-the-art approach.

Precipitation change was the most substantial parts of the hydrologic cycle and regarded as the important performance of climate change. Thus, understanding the precipitation change in the arid area was a key problem in the background of global climate change. In this study, precipitation was investigated by the means of Sen’s Slope and Mann-Kendall. The results were as follows: The Northern Xinjiang was getting wetter than before. The annual precipitation of the snow period of all 28 meteorological stations was increasing by the average rate at 0.642mm/y; the biggest slope occurred in November at the level of 0.167mm/y, the March was smallest at 0.07mm/y. This study could help us understand more about the precipitation change in the arid area.

Cross-Technology Communication (CTC) is an emerging technology to support direct communication between wireless devices that follow different standards. In spite of the many different proposals from the community to enable CTC, the performance aspect of CTC is an equally important problem but has seldom been studied before. We find this problem is extremely challenging, due to the following reasons: on one hand, a link for CTC is essentially different from a conventional wireless link. The conventional link indicators like RSSI (received signal strength indicator) and SNR (signal to noise ratio) cannot be used to directly characterize a CTC link. On the other hand, the indirect indicators like PER (packet error rate), which is adopted by many existing CTC proposals, cannot capture the short-term link behavior. As a result, the existing CTC proposals fail to keep reliable performance under dynamic channel conditions. In order to address the above challenge, we in this paper propose AdaComm, a generic framework to achieve self-adaptive CTC in dynamic channels. Instead of reactively adjusting the CTC sender, AdaComm adopts online learning mechanism to adaptively adjust the decoding model at the CTC receiver. The self-adaptive decoding model automatically learns the effective features directly from the raw received signals that are embedded with the current channel state. With the lossless channel information, AdaComm further adopts the fine tuning and full training modes to cope with the continuous and abrupt channel dynamics. We implement AdaComm and integrate it with two existing CTC approaches that respectively employ CSI (channel state information) and RSSI as the information carrier. The evaluation results demonstrate that AdaComm can significantly reduce the SER (symbol error rate) by 72.9% and 49.2%, respectively, compared with the existing approaches.

This paper presents a 10 cm × 5 cm × 5 cm, 52 g one-motor driven robot. One DC motor with a driving gear drives two driven gears to implement the functions of jumping and walking. Two one-way bearings mounted on the inner races of the two driven gears are used to switch between jumping and walking when the motor rotates clockwise and anticlockwise respectively. The jumping energy is obtained by compressing and releasing two torsion springs using a cylindrical cam with quick return characteristics. Two disk cams drive two forelegs with elastic joints to step forward one after another to implement the walking locomotion pattern. Two connecting rods link the forelegs and the rear legs on the left and right sides of the robot to transmit motions from forelegs to rear legs. The jumping and walking performances of the robot are tested. Experimental results show that the proposed robot can jump more than 33 cm high at a takeoff angle of 71.2° and it can walk forward at 1.43 mm/s.

We propose a distributed security architecture for incorporation into object oriented distributed computing systems, and in particular, into OMG's CORBA based object architectures. The primary objective of the security architecture is to make CORBA resilient to both component failures and malicious attacks. The core of the architecture is the notion of secure ORB node-an ORB node enhanced with "pluggable" system security objects interacting through generic security service APIs. System security objects coupled with protocols among them facilitate creation and management of clients, objects, and security information. Security services addressed in the paper include, but are in no way limited to, client/object authentication, access control, and integrity and confidentiality protections.>

The application of probabilistic reasoning to fault diagnosis in linear lightwave networks (LLNs) is investigated. The LLN inference model is represented by a Bayesian network (or causal network). An inference algorithm is proposed that is capable of conducting fault diagnosis (inference) with incomplete evidence and on an interactive basis. Two belief updating algorithms are presented which are used by the inference algorithm for performing fault diagnosis. The first belief updating algorithm is a simplified version of the one proposed by Pearl (1988) for singly connected inference models. The second belief updating algorithm applies to multiply connected inference models and is more general than the first. The authors also introduce a t-fault diagnosis system and an adaptive diagnosis system to further reduce the computational complexity of the fault diagnosis process.>

Earlier work of P. Gacs and J. Reif (see J. Comput. Syst. Sci., vol.36, no.2, p.125-147 (1988)) on reliable computation using cellular automata is extended to asynchronous cellular automata. The goal is to find ways to implement computations of arbitrary size by a homogeneous asynchronous array of unreliable elementary components. An asynchronous two-dimensional cellular automaton is constructed so that given any computation and reliability requirement, a program can be found for such an automaton that performs the computation with probability that meets the reliability requirement. This is the strongest among the published results on reliable computation in an asynchronous environment. It is stronger than its asynchronous counterpart in the sense that it removes the assumption of a fault-free global synchronization clock underlying a synchronous system.>