Fallback Mechanism Research Articles

A Survey on Human in the Loop for Self-Adaptive Systems

Adaptive systems possess the remarkable capability to assess and modify their behavior in real-time, particularly when the software deviates from its programmed course or when opportunities for enhanced functionality or performance arise. This adaptability proves invaluable in highly dynamic environments, where rapid changes occur, and human oversight alone falls short in effectively managing applications. However, in certain system types, achieving optimal performance through adaptation may necessitate human input, be it as a sensor providing unique information beyond the system’s reach, an actuator driving adaptation, or a fallback mechanism in contingency scenarios. In this context, the concept of ’human-in-the-loop’ harnesses the innate capabilities of humans to execute tasks and make decisions with greater efficiency and precision, thereby ensuring the security and reliability of these systems. Our primary objective in this study is to present a comprehensive analysis of the key research and contributions in this field. Additionally, we aim to pinpoint potential research avenues and unresolved challenges within the realm of Human-in-the-loop.

Design and simulation of a smart master switch system based on multi-input XOR logic gate

Mechanical switches have been the conventional way of controlling electrical energy in different electrical systems such as: lighting systems, socket systems, and circuit breakers, especially in domestic, hospital, and industrial applications. Mechanical switches often require physical access to control different electrical devices that are connected to the power sources. The work presented in this paper aimed at designing and simulating a multi-input based Exclusive-Or master switch system that remotely controls the lighting and socket systems using the wireless switching mechanisms of Global System for Mobile communication and Bluetooth. The system therefore limits physical interaction that require use of the single pole double throw and keypad mechanical switches that are however included to act as fall-back mechanism in control of electrical devices. Within the recommended electrical safety measures, the design can be streamlined and integrated to remotely control lighting systems and sockets alongside the conventional mechanical switches in the consumer control unit.

Diagnostic Concepts for Condition Monitoring and Fallback Mechanisms in Vehicle Electrical Systems

Diagnostic Concepts for Condition Monitoring and Fallback Mechanisms in Vehicle Electrical Systems

An Automated Parametric Surface Patch-Based Construction Method for Smooth Lattice Structures with Irregular Topologies

Additive manufacturing enables the realization of complex component designs that cannot be achieved with conventional processes, such as the integration of cellular structures, such as lattice structures, for weight reduction. To include lattice structures in component designs, an automated algorithm compatible with conventional CAD that is able to handle various lattice topologies as well as variable local shape parameters such as strut radii is required. Smooth node transitions are desired due to their advantages in terms of reduced stress concentrations and improved fatigue performance. The surface patch-based algorithm developed in this work is able to solidify given lattice frames to smooth lattice structures without manual construction steps. The algorithm requires only a few seconds of sketching time for each node and favours parallelisation. Automated special-case workarounds as well as fallback mechanisms are considered for non-standard inputs. The algorithm is demonstrated on irregular lattice topologies and applied for the construction of a lattice infill of an aircraft component that was additively manufactured.

Finding optimal decision boundaries for human intervention in one-class machine-learning models for industrial inspection

Abstract Anomaly detection with machine learning in industrial inspection systems for manufactured products relies on labelled data. This raises the question of how the labelling by humans should be conducted. Moreover, such a system will most likely always be imperfect and potentially need a human fall-back mechanism for ambiguous cases. We consider the case where we want to optimise the cost of the combined inspection process done by humans together with a pre-trained algorithm. This gives improved combined performance and increases the knowledge of the performance of the pre-trained model. We focus on so-called one-class classification problems which produce a continuous outlier score. After establishing some initial setup mechanisms ranging from using prior knowledge to calibrated models, we then define some cost model for machine inspection with a possible second inspection of the sample done by a human. Further, we discuss in this cost model how to select two optimal boundaries of the outlier score, where in between these two boundaries human inspection takes place. Finally, we frame this established knowledge into an applicable algorithm and conduct some experiments for the validity of the model.

Unified Automatic Control of Vehicular Systems With Reinforcement Learning

Emerging vehicular systems with increasing proportions of automated components present opportunities for optimal control to mitigate congestion and increase efficiency. There has been a recent interest in applying deep reinforcement learning (DRL) to these nonlinear dynamical systems for the automatic design of effective control strategies. Despite conceptual advantages of DRL being model-free, studies typically nonetheless rely on training setups that are painstakingly specialized to specific vehicular systems. This is a key challenge to efficient analysis of diverse vehicular and mobility systems. To this end, this article contributes a streamlined methodology for vehicular microsimulation and discovers high performance control strategies with minimal manual design. A variable-agent, multi-task approach is presented for optimization of vehicular Partially Observed Markov Decision Processes. The methodology is experimentally validated on mixed autonomy traffic systems, where fractions of vehicles are automated; empirical improvement, typically 15-60% over a human driving baseline, is observed in all configurations of six diverse open or closed traffic systems. The study reveals numerous emergent behaviors resembling wave mitigation, traffic signaling, and ramp metering. Finally, the emergent behaviors are analyzed to produce interpretable control strategies, which are validated against the learned control strategies. Note to Practitioners—As vehicular systems such as real-world traffic systems and robotic warehouses become increasingly automated, optimizing vehicle movements sees an increasing potential to reduce congestion and increase efficiency. For many vehicular systems, simulations of varying fidelity are commonly used for analysis and optimization without the need to deploy real vehicles. This article describes a unified and practical approach for optimal control of vehicles in arbitrary simulated vehicular systems while permitting partial automation, where the behavior of fractions of vehicles at given times can be modelled but not controlled. As illustrated by the diverse traffic systems considered in this article, the presented methodology emphasizes ease of application within any simulated vehicular system while minimizing manual efforts by the practitioner. The control inputs consist of local information around each automated vehicle, while the control outputs are commands for longitudinal acceleration and lateral lane change. Experimental results are presented for relatively small simulated traffic systems, though the methodology can be adapted to larger vehicular systems with minor modifications. Experimentally optimized behaviors provide insights to the practitioner which may assist in designing simplified and interpretable control strategies. Implementation in real-world systems depends on two requirements: 1) a reliable fallback mechanism for ensuring safety of vehicles, and 2) sufficient fidelity of the simulator for simulated behaviors to transfer. These requirements are under active research for traffic systems and may be practical in some robotic settings. To facilitate robust transfer of policies from simulated to real-world systems, future extensions of this work may inject additional randomization into simulation while reducing the unmodeled stochasticity of targeted real-world systems as much as possible.

TZEAMM: An Efficient and Secure Active Measurement Method Based on TrustZone

With the rapid development of computer and communication technology, embedded systems are widely used in smart devices. The increasing connectivity of these systems and the difficulties in providing comprehensive security have made such devices vulnerable to malicious attacks. Passive defense technologies and traffic-based intrusion detection technologies are not fully effective against such attacks. Trusted execution environment (TEE) technology can ensure system security against unknown attacks to some extent. Most researchers use TrustZone to implement TEE. However, the problem is that the API interface of the TEE module which provides the service is not secure. Therefore, to actively defend against attacks, we developed a trusted computing active measurement architecture based on TrustZone. To overcome the serious problem that modules in the trusted execution environment need to be passively invoked to provide services, we have proposed an active measurement closed-loop immune mechanism. To reduce the trusted computing base and reduce the performance overhead, we removed certain functional modules from the trusted execution environment. In addition, based on this architecture, we developed a trust chain and dynamic measurement method to ensure the security of the target applications. We changed the traditional attack response method, which requires the entire system to be restarted after an attack, by developing a fallback mechanism that is more suitable for the system. Finally, we verified the effectiveness of the architecture by developing an attack model. Performance testing and analysis showed that the architecture reduced the impact of the security mechanisms on the system. In the future, we will extend our research to more fine-grained measurements.

Three-Dimensional Image Reconstruction With Enhanced Spatial Accuracy for Pulsed-Laser Imaging Based on Compressive Sensing

Compressive sensing (CS) has been intensively studied as a superresolution technique in image acquisition and reconstruction. It has also been introduced into three-dimensional (3-D) laser imaging to enhance the spatial resolution of images obtained from existing few-pixel array sensors. These approaches, however, either suffer from low range resolution or lack long-range detecting abilities. To solve the CS reconstruction problem under the 3-D pulsed-laser imaging framework, we build a sampling model to combine the CS sampling process with the time-of-flight-based range measurement procedure in pulsed-laser imaging. To explore range information from the overlapping echo signals, we develop a gradual alternating minimization algorithm with a designed range equivalent matrix, in which the feasible region of the solution gradually shrinks to make the reconstruction procedure tractable. To further enhance the quality of the reconstructed intensity image, we introduce rank minimization and a fallback mechanism, in which the sparsity and stability of the reconstructed intensity image is refined. In this manner, the accuracy of reconstructed 3-D images can be improved in both the pixel direction and range direction. The simulation results based on real datasets demonstrate that our proposed scheme can yield superior accurate reconstruction and leads to significant improvements compared with recent approaches.

Swap Rate fallback: unreasonable effectiveness of approximations and alternatives

Observes that cash-settled swaptions with collateral discounting are impacted by the Swap Rate fallback mechanisms decided by working groups/ISDA. The legacy vanilla swaptions are becoming exotic products, as the mechanism is based on a non-linear transformation of the OIS swap rate, and generate convexity adjustments. It turns out that those two effects almost cancel each other and lead to almost vanilla products. Henrard analyses those cancelling effects and the risk management impacts. Based on those insights, Henrard proposes an adjusted fallback mechanism that reduces further the exotic features and simplifies further the risk management of the legacy book.

Memory-optimized multi-version concurrency control for disk-based database systems

Pure in-memory database systems offer outstanding performance but degrade heavily if the working set does not fit into DRAM, which is problematic in view of declining main memory growth rates. In contrast, recently proposed memory-optimized disk-based systems such as Umbra leverage large in-memory buffers for query processing but rely on fast solid-state disks for persistent storage. They offer near in-memory performance while the working set is cached, and scale gracefully to arbitrarily large data sets far beyond main memory capacity. Past research has shown that this architecture is indeed feasible for read-heavy analytical workloads. We continue this line of work in the following paper, and present a novel multi-version concurrency control approach that enables a memory-optimized disk-based system to achieve excellent performance on transactional workloads as well. Our approach exploits that the vast majority of versioning information can be maintained entirely in-memory without ever being persisted to stable storage, which minimizes the overhead of concurrency control. Large write transactions for which this is not possible are extremely rare, and handled transparently by a lightweight fallback mechanism. Our experiments show that the proposed approach achieves transaction throughput up to an order of magnitude higher than competing disk-based systems, confirming its viability in a real-world setting.

Intelligent Medicine Kit for Healthcare Monitoring, An IOT Based Solution

Increase in chronic diseases worldwide demands efficient healthcare solutions for maintaining well-beingof people. Treatment requires timely in-take of medicines and strict adherence to routine. This lack of adherenceis estimated to cause many deaths and hospitalizations. If we can get people to take their medications regularly,they won’t develop complications. In addition to improving patient outcomes, medication adherence will reducehealth care costs associated with these conditions. Healthcare monitoring solutions based on Internet of Things(IoT) technology has drawn significant research attention. This paper proposes an IoT based user configurablecustomized intelligent medicine kit augmented with Wi-Fi and Bluetooth Low Energy technologies. It hascapability to detect whether patient is taking all prescribed medicines on fix schedule and intelligentlycommunicates the same to patient and their close relatives using uniquely created Four Tier Notification System(FTNS), thus helping patient to live a healthy life. The paper discusses a novel theme on the functioning of amedical grade device which would consume information from sensor and send it to the central server with amaximum possibility of success using Four Tier Notification System (FTNS). This novel approach discusseshandshaking of connection to the central serve with a fallback mechanism to achieve maximum success of datasynchronization. This paper also discusses how this useful data from medicine kit can help healthcare sector toclosely track patient's physical activities and helps to influence the way healthcare sector operates in future.

ICDRP-F-SDVN: An innovative cluster-based dual-phase routing protocol using fog computing and software-defined vehicular network

Over time, wireless technology is evolving impulsively. For the most part, studies presently focused on the telecommunication field. Vehicular Ad-hoc Network (VANET) is one of the leading genuine and appealing areas for Intelligent Transportation Systems (ITS) originators and organizations. Essentially, VANET is an ad-hoc network consisting of several vehicles that flow on the same road. The existence of such networks revolutionizes the wireless communication field. The immense increase in the number of smart vehicles throughout time is the reason behind the massive evolution in VANET technologies. VANETs requirements are evolving quickly, therefore, having more flexible, scalable, and well-connected routes in VANETs is becoming essential in any new routing protocol. Antiquated VANETs routing protocols and designs encounter very challenging technical, employment, and control problems with the evolution of VANETs requirements. This paper presents a new efficient routing protocol for VANETs, namely, Innovative Cluster-Based Dual-Phase Routing Protocol Using Fog Computing and Software-Defined Vehicular Network (ICDRP-F-SDVN) which utilizes state-of-the-art technologies to overcome the drawbacks of traditional VANETs routing protocols. The use of fog computing and Software-Defined Networks (SDN) combined in our protocol presents a robust new architecture that meets all the new requirements and overcomes challenges arising from technological development and rapid escalation in the number of smart vehicles. Additionally, we propose a novel clustering algorithm mainly for the reason of lessening the long-distance communications in each cluster. In conjunction with this clustering algorithm, we propose an efficient control overhead reduction mechanism, thereby lowering the messages' exchanges imposed. Interestingly, our proposed protocol is a dual-phase protocol with a 90% of SDN packet delivery ratio. In other words, when the SDN fails to deliver packets, traditional Ad-hoc On-Demand Distance Vector (AODV) handles them as a fallback mechanism. In this paper, the performance of ICDRP-F-SDVN is evaluated in terms of throughput and end-to-end delay and further compared with four closely related routing protocols, that are, Intersection Dynamic VANET Routing (IDVR), Vehicle Density and Load Aware (VDLA), Intelligent Routing Protocol Using Real-Time Traffic Information in Urban Vehicular Environment (IRTIV), and Greedy Perimeter Coordinator Routing (GPCR). In terms of control overhead, the comparison is conducted with Control Overhead Reduction Algorithm (CORA), Moving Zone-Based Routing Protocol (MoZo), Beaconless Routing Algorithm for Vehicular Environment (BRAVE), and Cluster-Based Directional Routing Protocol (CBDRP). Our proposed protocol has achieved impressive simulation results compared to previously mentioned protocols. In particular, the throughput is increased by 8277%, 64309%, 76235%, and 96053% while the end-to-end delay is reduced by 77.5%, 82.5%, 87.2%, 95.6% over IDVR, VDLA, IRTIV, and GPCR, respectively. Finally, the control overhead is lowered by 42.3%, 87%, 93.5%, 96.7% over that of CORA, MoZo, BRAVE, and CBDRP, respectively.

A Failsoft Scheme for Mobile Live Streaming by Scalable Video Coding

In this study, we propose a mechanism called adaptive failsoft control to address peak traffic in mobile live streaming, using a chasing playback function. Although a cache system is avaliable to support the chasing playback function for live streaming in a base station and device-to-device communication, the request concentration by highlight scenes influences the traffic load owing to data unavailability. To avoid data unavailability, we adapted two live streaming features: (1) streaming data while switching the video quality, and (2) time variability of the number of requests. The second feature enables a fallback mechanism for the cache system by prioritizing cache eviction and terminating the transfer of cache-missed requests. This paper discusses the simulation results of the proposed mechanism, which adopts a request model appropriate for (a) avoiding peak traffic and (b) maintaining continuity of service.

From the Concept of Being “the Boss” to the Idea of Being “a Team”: The Adaptive Co-Pilot as the Enabler for a New Cooperative Framework

The “classical” SAE LoA for automated driving can present several drawbacks, and the SAE-L2 and SAE-L3, in particular, can lead to the so-called “irony of automation”, where the driver is substituted by the artificial system, but is still regarded as a “supervisor” or as a “fallback mechanism”. To overcome this problem, while taking advantage of the latest technology, we regard both human and machine as members of a unique team that share the driving task. Depending on the available resources (in terms of driver’s status, system state, and environment conditions) and considering that they are very dynamic, an adaptive assignment of authority for each member of the team is needed. This is achieved by designing a technology enabler, constituted by the intelligent and adaptive co-pilot. It comprises (1) a lateral shared controller based on NMPC, which applies the authority, (2) an arbitration module based on FIS, which calculates the authority, and (3) a visual HMI, as an enabler of trust in automation decisions and actions. The benefits of such a system are shown in this paper through a comparison of the shared control driving mode, with manual driving (as a baseline) and lane-keeping and lane-centering (as two commercial ADAS). Tests are performed in a use case where support for a distracted driver is given. Quantitative and qualitative results confirm the hypothesis that shared control offers the best balance between performance, safety, and comfort during the driving task.

LIBOR Prompts Quantile Leap: Machine Learning for Quantile Derivatives

Inspired by initially proposed IBOR fallback mechanisms, we show how deep learning can be used to quickly and accurately compute the {expected median} of a time series at future inference dates with varying amounts of observed data. While the IBOR fallback spreads were ultimately fixed, the technique outlined here showcases the ability of neural networks to tackle financial problems over seemingly impossibly large domains.

Understanding users’ perceptions to improve fallback authentication

Despite receiving a lot of scrutiny and criticism, security questions are still widely adopted. Although new techniques are continuously being proposed to improve fallback authentication (i.e. security questions design), little research investigated users’ security and memorability perceptions. Previous research found that users’ perceptions are important because they can impact the adoption of security techniques. Hence, this research contributes to security questions research by investigating (with a study of n = 30) how users select security questions, what strategies are used to memorize answers, how users perceive the security and memorability of their answers and how a technique which addresses key security weaknesses (but makes them less memorable) impacts users’ perceptions. Our key findings reveal that despite asking participants to select security questions for an online banking scenario, participants who answered security questions with their own answers did not consider security factors. Instead, they selected easy, truthful and certain answers. Memorization strategies were ignored by most participants (even those who used unfamiliar answers). We also found that a technique designed to address key security weaknesses seemed to inspire some kind of security awareness (but would still not be enough). Based on these findings this paper provides recommendations to improve the design of security questions, strengthening fallback authentication mechanisms secure and usable.

System for software-controlled switch with manual override

Smart switch with manual override capability provides a fallback mechanism for users to control household appliances in the event of electronics failure. Essentially, the device is equipped with a single-pole double-throw (SPDT) mechanical switch coupled with electronically-controlled relay. Load current is measured through current sensor connected to the return lines and is constantly read by microcontroller to determine the operational state of the switch and to detect over-current fault. A hardware prototype was developed that uses open-source software to ease system development. Lab testing verifies functional operation of the prototype meets design objectives.

A fallback mechanism or a commander? A discussion about the role and skill needs of future drivers within partially automated vehicles

Vehicle automation promises to improve safety. However, it also raises concerns about human performance. Many current research and policy efforts address automation effects on operational levels of driving control. However, the driving task encompasses more than just operational control. So, in this paper, we ask whether a more holistic view of the driving task should be considered. Would such a framework imply that the human role is more than just a “fallback” mechanism for automation systems, especially for partial automation systems? If the human role were conceptualized more as a “commander role” (than an “operator”), would it enable system-level performance to be optimized more effectively? If so, would this lead to changes in design of the vehicle and interface—as well as in skills needed by human users? New skills like strategic planning, delegation of control responsibility, tactical avoidance, and effective mediation of hazardous situations could become important. A more holistic approach for integrating automation may facilitate role change for human users while also improving safety.

STREAM: Medium Access Control With Station Presence Awareness in Crowded Networks

In this article, we propose a medium access control (MAC) scheme, called STation pREsence-Aware MAC (STREAM), that offers equal contention opportunities to the stations in a network. STREAM is designed to preserve its performance in crowded and high-traffic networks. The scheme improves channel access, utilization, and service fairness of stations. STREAM detects collided transmissions or idle events in the network to estimate the number of active stations. STREAM sets the contention period as a function of the number of stations in the network. As a result, the number of contention opportunities is enlarged and the access to the transmission channel in the network is improved. In this way, STREAM overcomes the performance degradation of IEEE 802.11 under heavy traffic and crowded networks. We analyze the throughput of STREAM, fairness of service, and the probability that a station experiences collisions as a function of the number of stations in the network. We also present performance evaluations and comparisons with IEEE 802.11 and other high-performing schemes. We extend the application of STREAM on traffic with different priorities and quality of service. Our evaluations show that STREAM outperforms IEEE 802.11 and the compared schemes. We show that STREAM is an ideal candidate as a fallback mechanism for IEEE 802.11 under overwhelming conditions.

Cranial Perforation Using an Optically-Enhanced Surgical Drill.

The design of mechanically clutched cranial perforators, used in craniotomy procedures, limits their performance under certain clinical conditions and can, in some cases, impose the risk of severe brain injury on patients undergoing the procedure. An additional safety mechanism could help in mitigating these risks. In this work, we examine the use of diffuse reflectance spectroscopy as a potential fallback mechanism for near real-time detection of the bone-brain boundary. Monte Carlo simulation of a two layer model with optical properties of bone and brain at 530 and 850nm resulted in a detectable change in diffuse reflectance signal when approaching the boundary. The simulated results were used to guide the development of an experimental drill control system, which was tested on 10 sheep craniums and yielded 88.1 % success rate in the detection of the approaching bone-brain boundary.