Transparent and consistent reporting of user studies is essential for advancing scientific knowledge. In human-robot interaction (HRI), studies are often reported incompletely, even in top-tier venues, limiting proper evaluation, replication, and practical application of findings in practice. This study aimed to generate expert consensus on a reporting checklist for HRI user studies and provide a validated tool to improve transparency, reproducibility, and methodological rigor in the field, leading to easier translation of research into practice. A two-round Delphi study was conducted with 34 HRI experts from academia and industry from over 12 countries. An international panel of nine interdisciplinary experts first synthesized a preliminary list of 116 reporting items from the literature. Experts rated the importance of each item and provided qualitative feed- back. Consensus was defined as 70% agreement, and items were iteratively refined through anonymous online surveys. Overall, consensus was achieved on 106 items, encompassing both essential and context-dependent elements in nine domains. The resulting RUSH checklist (Reporting User Studies in Human-Robot Inter- action) provides the first community-endorsed, consensus-based reporting guideline for HRI user studies.

As AI technologies become increasingly integrated into online medical consultations, understanding patient responses to different forms of AI involvement is crucial. However, limited research has examined how patients’ perceptions of AI involvement affect their intention to comply with medical advice. Drawing on the heuristic–systematic model, this study examines how agent identity (human, AI, or AI-assisted human) influences patients’ intention to comply with medical advice. Across four scenario-based experiments, results show that patients are more likely to comply with advice from AI-assisted human agents than from AI agents, but less likely than with human agents. Perceived decision-maker autonomy mediates this effect. Moreover, we identify two boundary conditions: the effect of perceived decision-maker autonomy is weakened when decision transparency is high but strengthened when disease severity is high. These findings advance understanding of human–AI collaboration and offer practical insights to enhance patient acceptance of AI in online consultations.

Augmented reality (AR) allows visualizations to be situated where they are relevant, e.g., in a robot's operating environment or task space. Yet, headset-based AR suffers a scalability issue because every viewer must wear a headset. Projector-based spatial AR solves this problem by projecting augmentations onto the scene, e.g., recognized objects or navigation paths, viewable to crowds. However, this solution mostly requires vertical flat surfaces that may not exist in large open areas like auditoriums, warehouses, construction sites, or search and rescue scenes. Moreover, when humans are not co-located with the robot or situated at a distance, the projection may not be legible to humans. Thus, there is a need to create a projectable, viewable surface for humans in such scenarios. In this HRI systems paper, we introduce a fog screen-robot system that integrates a mid-air fog screen device into a robot to create such a projectable surface and presents two evaluations in a construction site and a search and rescue scenario for high-stakes communication needs. Specifically, we implemented an existing fog screen device, which can only project one-third of a meter (33cm). We improved it to achieve a fog screen length of half a meter (53cm). In the noisy construction site scenario, the robot inspected the site and projected icons for missing wall sockets and plumbing fixtures. In the unstructured search and rescue scenario, the robot was able to project a person icon for a first responder to save life. All 3D models and code are available at https://github.com/TheRARELab/fog-screen-robot-system. Videos are available at https://osf.io/b4efu/.

As robots are deployed into large-scale human environments, they will need to engage in task-oriented dialogues about objects and locations beyond those that can currently be seen. In these contexts, speakers use a wide range of referring gestures beyond those used in the small-scale interaction contexts that HRI research typically investigates. In this work, we thus seek to understand how robots can better generate gestures to accompany their referring language in large-scale interaction contexts. In service of this goal, we present the results of two human-subject studies: (1) a human-human study exploring how human gestures change in large-scale interaction contexts, and to identify human-like gestures suitable to such contexts yet readily implemented on robot hardware; and (2) a human-robot study conducted in a tightly controlled Virtual Reality environment, to evaluate robots' use of those identified gestures. Our results show that robot use of Precise Deictic and Abstract Pointing gestures afford different types of benefits when used to refer to visible vs. non-visible referents, leading us to formulate three concrete design guidelines. These results highlight both the opportunities for robot use of more humanlike gestures in large-scale interaction contexts, as well as the need for future work exploring their use as part of multi-modal communication.

As robots tackle increasingly complex tasks, the need for explanations becomes essential for gaining trust and acceptance. Explainable robotic systems should not only elucidate failures when they occur but also predict and preemptively explain potential issues. This paper compares explanations from Reactive Systems, which detect and explain failures after they occur, to Proactive Systems, which predict and explain issues in advance. Our study reveals that the Proactive System fosters higher perceived intelligence and trust and its explanations were rated more understandable and timely. Our findings aim to advance the design of effective robot explanation systems, allowing people to diagnose and provide assistance for problems that may prevent a robot from finishing its task.

We are witnessing increasing calls for reproducibility and replicability in HRI studies to improve reliability and confidence in empirical findings. One solution to facilitate this is using a robot platform that researchers frequently use, making it easier to replicate studies to verify results. In this work, we focus on a popular, affordable, and rich-in-functionality robot platform, NAO/Pepper, and contribute a generalized experiment project specifically for conducting language-based HRI experiments where a robot instructs a human for a task, including objective data collection.

In this article, a power-on-reset (POR) circuit with accurate trigger-point voltage and ultralow typical quiescent current for emerging nonvolatile memory (NVM) is presented. To keep the trigger-point voltage from the effect of process, voltage, and temperature (PVT) and supply ramp rate variations, low-cost current generators and a current comparator are adopted with brown-out detection (BOD). A protection circuit is utilized for correct operations in different BOD events. Meanwhile, delay blocks are utilized to generate reliable pulse signals that are less influenced by temperature and supply ramp rate. The simulation results show that the trigger-point voltage is 2.08 V with a temperature coefficient (TC) of 81.8 ppm/$^{\circ}$C and a deviation to the ramp time of 9.91% for a wide ramp time range from 10$\mu $s to dc. In addition, the reset duration time of the POR pulse is also reliable. The proposed POR circuit designed in the 55-nm CMOS process consumes only 0.65-nA typical quiescent current and 59$\times$146$\mu $m area, which is suitable for emerging NVM systems.

Augmented Reality (AR) technologies present an exciting new medium for human-robot interactions, enabling new opportunities for both implicit and explicit human-robot communication. For example, these technologies enable physically-limited robots to execute non-verbal interaction patterns such as deictic gestures despite lacking the physical morphology necessary to do so. However, a wealth of HRI research has demonstrated real benefits to physical embodiment (compared to, e.g., virtual robots on screens), suggesting AR augmentation of virtual robot parts could face challenges. In this work, we present empirical evidence comparing the use of virtual (AR) and physical arms to perform deictic gestures that identify virtual or physical referents. Our subjective and objective results demonstrate the success of mixed reality deictic gestures in overcoming these potential limitations, and their successful use regardless of differences in physicality between gesture and referent. These results help to motivate the further deployment of mixed reality robotic systems and provide nuanced insight into the role of mixed-reality technologies in HRI contexts.

Deployed social robots are increasingly relying on wakeword-based interaction, where interactions are human-initiated by a wakeword like "Hey Jibo". While wakewords help to increase speech recognition accuracy and ensure privacy, there is concern that wakeword-driven interaction could encourage impolite behavior because wakeword-driven speech is typically phrased as commands. To address these concerns, companies have sought to use wakeword design to encourage interactant politeness, through wakewords like "Name?, please". But while this solution is intended to encourage people to use more "polite words", researchers have found that these wakeword designs actually decrease interactant politeness in text-based communication, and that other wakeword designs could better encourage politeness by priming users to use Indirect Speech Acts. Yet there has been no previous research to directly compare these wakewords designs in in-person, voice-based human-robot interaction experiments, and previous in-person HRI studies could not effectively study carryover of wakeword-driven politeness and impoliteness into human-human interactions. In this work, we conceptually reproduced these previous studies (n=69) to assess how the wakewords "Hey "Name"", "Excuse me "Name?", and "Name?, please" impact robot-directed and human-directed politeness. Our results demonstrate the ways that different types of linguistic priming interact in nuanced ways to induce different types of robot-directed and human-directed politeness.

Language-capable robots must be able to efficiently and naturally communicate about objects in the environment.A key part of communication is Referring Form Selection (RFS): the process of selecting a form like it, that, or the N to use when referring to an object.Recent cognitive status-informed computational RFS models have been evaluated in terms of goodness-of-fit to human data.But it is as yet unclear whether these models actually select referring forms that are any more natural than baseline alternatives, regardless of goodness-offit.Through a human subject study designed to assess this question, we show that even though cognitive status-informed referring selection models achieve good fit to human data, they do not (yet) produce concrete benefits in terms of naturality.On the other hand, our results show that human utterances also had high variability in perceived naturality, demonstrating the challenges of evaluating RFS naturality.

Mixed Reality provides a powerful medium for transparent and effective human-robot communication, especially for robots with significant physical limitations (e.g., those without arms). To enhance nonverbal capabilities for armless robots, this article presents two studies that explore two different categories of mixed reality deictic gestures for armless robots: a virtual arrow positioned over a target referent (a non-ego-sensitive allocentric gesture) and a virtual arm positioned over the gesturing robot (an ego-sensitive allocentric gesture). In Study 1, we explore the tradeoffs between these two types of gestures with respect to both objective performance and subjective social perceptions. Our results show fundamentally different task-oriented versus social benefits, with non-ego-sensitive allocentric gestures enabling faster reaction time and higher accuracy, but ego-sensitive gestures enabling higher perceived social presence, anthropomorphism, and likability. In Study 2, we refine our design recommendations by showing that in fact these different gestures should not be viewed as mutually exclusive alternatives, and that by using them together, robots can achieve both task-oriented and social benefits.

Robots need to explain their behavior to gain trust. Existing research has focused on explaining a robot’s current behavior, yet it remains unknown yet challenging how to provide explanations of past actions in an environment that might change after a robot’s actions, leading to critical missing causal information due to moved objects. We conducted an experiment (N = 665) investigating how a robot could help participants infer the missing causal information by replaying the past behavior physically, using verbal explanations, and projecting visual information onto the environment. Participants watched videos of the robot replaying its completion of an integrated mobile kitting task. During the replay, the objects are already gone, so participants needed to infer where an object was picked, where a ground obstacle had been, and where the object was placed. Based on the results, we recommend combining physical replay with speech and projection indicators (Replay-Project-Say) to help infer all the missing causal information (picking, navigation, and placement) from the robot’s past actions. This condition had the best outcome in both task-based—effectiveness, efficiency, and confidence—and team-based metrics—workload and trust. If one’s focus is efficiency, then we recommend projection markers for navigation inferences and verbal markers for placing inferences.

In many domains, robots must be able to commu-nicate to humans through natural language. One of the core capabilities needed for task-based natural language communication is the ability to refer to objects, people, and locations. Existing work on robot referring expression generation has focused nearly exclusively on generation of definite descriptions to visible objects. But humans use many other linguistic forms to refer (e.g., pronouns) and commonly refer to objects that cannot be seen at time of reference. Critically, existing corpora used for modeling robot referring expression generation are insufficient for modeling this wider array of referring phenomena. To address this research gap, we present a novel interaction task in which an instructor teaches a learner in a series of construction tasks that require repeated reference to a mixture of present and non-present objects. We further explain how this task could be used in principled data collection efforts.

For mobile robots, mobile manipulators, and autonomous vehicles to safely navigate around populous places such as streets and warehouses, human observers must be able to understand their navigation intent. One way to enable such understanding is by visualizing this intent through projections onto the surrounding environment. But despite the demonstrated effectiveness of such projections, no open codebase with an integrated hardware setup exists. In this work, we detail the empirical evidence for the effectiveness of such directional projections, and share a robot-agnostic implementation of such projections, coded in C++ using the widely-used Robot Operating System (ROS) and rviz. Additionally, we demonstrate a hardware configuration for deploying this software, using a Fetch robot, and briefly summarize a full-scale user study that motivates this configuration. The code, configuration files (roslaunch and rviz files), and documentation are freely available on GitHub at https://github.com/umhan35/arrow_projection.

Language-capable robots require moral competence, including representations and algorithms for moral reasoning and moral communication. We argue for an ethical pluralist approach to moral competence that leverages and combines disparate ethical frameworks, and specifically argue for an approach to moral competence that is grounded not only in Deontological norms (as is typical in the HRI literature) but also in Confucian relational roles. To this end, we introduce the first computational approach that centers relational roles in moral reasoning and communication, and demonstrate the ability of this approach to generate both context-oriented and role-oriented explanations for robots' rejections of norm-violating commands, which we justify through our pluralist lens. Moreover, we provide the first investigation of how computationally generated role-based expla-nations are perceived by humans, and empirically demonstrate (N=120) that the effectiveness (in terms of of trust, understanding confidence, and perceived intelligence) of explanations grounded in different moral frameworks is dependent on nuanced mental modeling of human interlocutors.

Robots that use natural language in collaborative tasks must refer to objects in their environment. Recent work has shown the utility of the linguistic theory of the Givenness Hierarchy (GH) in generating appropriate referring forms. But before referring expression generation, collaborative robots must determine the content and structure of a sequence of utterances, a task known as document planning in the natural language generation community. This problem presents additional challenges for robots in situated contexts, where described objects change both physically and in the minds of their interlocutors. In this work, we consider how robots can “think ahead” about the objects they must refer to and how to refer to them, sequencing object references to form a coherent, easy to follow chain. Specifically, we leverage GH to enable robots to plan their utterances in a way that keeps objects at a high cognitive status, which enables use of concise, anaphoric referring forms. We encode these linguistic insights as a mixed integer program within a planning context, formulating constraints to concisely and efficiently capture GH-theoretic cognitive properties. We demonstrate that this GH-informed planner generates sequences of utterances with high intersentential coherence, which we argue should enable substantially more efficient and natural human-robot dialogue.

Robot interfaces often only use the visual channel. Inspired by Wickens’ Multiple Resource Theory, we investigated if the addition of audio elements would reduce cognitive workload and improve performance. Specifically, we designed a search and threat-defusal task (primary) with a memory test task (secondary). Eleven participants – predominantly first responders – were recruited to control a robot to clear all threats in a combination of four conditions of primary and secondary tasks in visual and auditory channels. While we did not find any statistically significant differences in performance or workload across subjects, making it questionable that Multiple Resource Theory could shorten longer-term task completion time and reduce workload. Our results suggest that considering individual differences for splitting interface modalities across multiple channels requires further investigation.

Despite the high configurability of IPs and hardware generators, code modifications are still required to introduce aspect-oriented instrumentation to satisfy emerging design requirements such as on-chip debug and functional safety. These code modifications lead to escalated development, verification efforts and deteriorate the code reuse. This paper proposes a highly efficient aspect-oriented design automation approach that leverages graph-grammar-based model transformations. With the proposed approach, main design functionalities and aspect-oriented instrumentation are separately developed, automatically integrated and verified. To demonstrate the applicability, industrial SoCs were transformed to support on-chip debug. Experimental results confirm the efficiency of the approach. Further, reduced code is needed with the proposed automation approach, which also replaces the error-prone manual RTL coding. Finally, the transformation scripts are applicable to different SoCs, which promotes the overall code reuse.

As autonomous robots continue to be deployed near people, robots need to be able to explain their actions. In this article, we focus on organizing and representing complex tasks in a way that makes them readily explainable. Many actions consist of sub-actions, each of which may have several sub-actions of their own, and the robot must be able to represent these complex actions before it can explain them. To generate explanations for robot behavior, we propose using Behavior Trees (BTs), which are a powerful and rich tool for robot task specification and execution. However, for BTs to be used for robot explanations, their free-form, static structure must be adapted. In this work, we add structure to previously free-form BTs by framing them as a set of semantic sets {goal, subgoals, steps, actions} and subsequently build explanation generation algorithms that answer questions seeking causal information about robot behavior. We make BTs less static with an algorithm that inserts a subgoal that satisfies all dependencies. We evaluate our BTs for robot explanation generation in two domains: a kitting task to assemble a gearbox, and a taxi simulation. Code for the behavior trees (in XML) and all the algorithms is available at github.com/uml-robotics/robot-explanation-BTs.

Although non-verbal cues such as arm movement and eye gaze can convey robot intention, they alone may not provide enough information for a human to fully understand a robot’s behavior. To better understand how to convey robot intention, we conducted an experiment ( N = 366 ) investigating the need for robots to explain , and the content and properties of a desired explanation such as timing , engagement importance , similarity to human explanations, and summarization . Participants watched a video where the robot was commanded to hand an almost-reachable cup and one of six reactions intended to show the unreachability : doing nothing (No Cue), turning its head to the cup (Look), or turning its head to the cup with the addition of repeated arm movement pointed towards the cup (Look & Point), and each of these with or without a Headshake. The results indicated that participants agreed robot behavior should be explained across all conditions, in situ , in a similar manner as what human explain, and provide concise summaries and respond to only a few follow-up questions by participants. Additionally, we replicated the study again with N = 366 participants after a 15-month span and all major conclusions still held.

The HW/SW interface is a common and crucial component in System-on-Chips, enabling the interaction between software and hardware. Generating architecture and firmware code of the interface from extended IP-XACT, SystemRDL, or proprietary formalism is an established technology. This paper describes a new area and performance optimization step in the HW/SW interface generation process that reduces the silicon area and hardware access time through firmware. Three improvements of the underlying formalism are applied to achieve the optimization: First, a decoupling of bit fields from registers, which allows the rearrangement of the memory layout easily. Second, the specification of hardware accesses, which constraints the bit field arrangement. Third, different implementations of bit field accesses, such as memory-mapped or via CPU special registers. The used generation framework follows the approach of model-driven architecture, which includes optimization. Initially, abstract models specify the requirements of the IP or the HW/SW interface. Transformations turn these models into platform-independent models of hardware and firmware. These models are further transformed into implementation-specific models of a target language, such as hardware description languages or C. The proposed optimization has been successfully applied to peripheral variants of a CPU subsystem used in an industrial demonstrator. An area reduction of 19% and a performance gain of 11% has been achieved by optimizing the interfaces.

The FetchIt! Mobile Manipulation Challenge, held at the IEEE International Conference on Robots and Automation (ICRA) in May 2019, offered an environment with complex and integrated task sets, irregular objects, confined space, and machining, introducing new challenges in the mobile manipulation domain. Here we describe our efforts to address these challenges by demonstrating the assembly of a kit of mechanical parts in a caddy. In addition to implementation details, we examine the issues in this task set extensively, and we discuss our software architecture in the hope of providing a base for other researchers. To evaluate performance and consistency, we conducted 20 full runs, then examined failure cases with possible solutions. We conclude by identifying future research directions to address the open challenges.

It has been claimed that a main advantage of cognitive architectures (compared to other types of specialized robotic architectures) is that they are task-general and can thus learn to perform any task as long as they have the right perceptual and action primitives. In this paper, we provide empirical evidence for this claim by directly comparing a high-performing custom robotic architecture developed for the standardized robotic "FetchIt!" challenge task to a hybrid cognitive robotic architecture that allows for online one-shot task learning and task modifications through natural language instructions. The results show that there is no disadvantage of running the hybrid architecture (i.e., no significant difference in overall performance or computational overhead compared to the custom architecture) while adding the flexibility of online one-shot task instruction and modification not available in the custom architecture.

This paper presents an automated process for end-to-end embedded system design following OMG's model driven architecture (MDA) vision. It tackles a major challenge in automation: bridging the large semantic gap between the specification and the target code. The shown MDA adaption proposes an uniform and systematic way by splitting the translation process into multiple layers and introducing design platform independent and implementation independent views.In our adaption of MDA, we start with a formalized specification and we end with code (view) generation. The code is then compiled (software) or synthesized (hardware) and finally assembled to the embedded system design. We split the translation process in Model-of-Thing (MoT), Model-of-Design (MoD) and Model-of-View (MoV) layers. MoTs represent the formalized specification, MoDs contain the implementation architecture in a view independent way, and MoVs are implementation dependent and view dependent, i.e., specific details in target language.MoT is translated to MoD, MoD is translated to MoV and MoV is finally used to generate views. The translation between the Models is based on templates, that reflect design and coding blueprints. The final step of the view generation is itself part of generation. The Model MoV and the unparse method are generated from a view language description.The approach has been successfully adapted for generating digital hardware (RTL), properties for verification (SVA), and snippets of firmware that have been successfully synthesized to an FPGA.

This paper presents a practical methodology for applying formal verification on industrial designs. The methodology is developed considering the quality, efficiency and productivity required in an industrial verification setup. The flow proposes a systematic approach addressing various aspects of the formal verification. First, the design implementation (RTL) is analyzed for its formal friendliness based on several predefined criteria. Next, a property automation flow is adapted for an efficient property development. Later, a series of verification tasks, grouped into formal test plan and formal execution plan are carried out to reach the formal sign-off stage. To demonstrate the applicability and effectiveness of the methodology, the proposed flow has been successfully applied on several industrial designs. In this paper, we consider the formal verification of Error Correction Codes, generally implemented in program and data flash memory interfaces to benchmark the proposed flow. Automatic property generation flow is used to generate an optimal property set with varying abstraction levels. The property proof runtimes are drastically reduced and better coverage compared to the previous hand-written properties has been achieved. New RTL bugs and specification errors have been found that were previously missed during the simulation.

Most research on human-robot handovers focuses on how the robot should approach human receivers and notify them of the readiness to take an object; few studies have investigated the effects of different release behaviors. Not releasing an object when a person desires to take it breaks handover fluency and creates a bad handover experience. In this paper, we investigate the effects of different release behaviors. Specifically, we study the benefits of a proactive release, during which the robot actively detects a human grasp effort pattern. In a 36-participant user study11The study is ready to reproduce with a Baxter robot. The code and environment setup is available at https://github.com/umhan35/handover_moveit, results suggest proactive release is more efficient than rigid release (which only releases when the robot is fully stopped) and passive release (the robot detects pulling by checking if a threshold value is reached). Subjectively, the overall handover experience is improved: the proactive release is significantly better in terms of handover fluency and ease-of-taking.

To cope with the strict reliability requirements of safety-critical ADAS applications, the upcoming TSN standard introduces mechanisms that enable transmission redundancy at any switch or end node. However, it is up to the designer to decide at which points and for which messages to activate transmission redundancy. This significantly increases the design space and requires to trade-off reliability with other routing-related design objectives like network load, transmission timing, or the monetary cost of the hardware. As a remedy, this paper a) presents two different exact approaches to generate feasible redundant message routings and b) proposes an extension of the state-of-the-art approach for the multi-objective routing optimization, enabling the optimizer to directly adjust system features that are relevant for the design objectives. A case study with an application from the automotive domain compares the optimization capabilities of the presented approaches for the routing generation and demonstrates the significant gain in optimization power that is achieved with the proposed optimization extension.

In this paper, a novel method of digital image encryption based on the reality-preserving multiple parameter fractional Hartley transform (RPMPFrHT) is proposed. Firstly, we define an RPMPFrHT that make sure the output of cryptosystem is real-value. Then, based on random address sequences generated by coupled logistic function, we propose the double random scrambling encoding scheme which scrambled an image in the spatial domain and the RPMPFrHT domain respectively. Our method can encrypt an original image into noise-like picture with real-value which is convenient for storage and transmission. Numerical simulations have been performed and demonstrated that the proposed image encryption method is effective and sensitive to keys. Moreover, some potential attacks have also been performed to verify the robustness of the proposed method.

Visual analytics on frequent web usage patterns aims to help users to (i) analyze the data so as to discover implicit, previously unknown and potentially useful information in the form of collections of frequently visited web pages in a single session and to (ii) visually represent the discovered knowledge so as to gain insight about the data. In this paper, we propose an interactive visual analytics tool (icVAT) for frequent pattern mining. It uses an orientation free, circular layout to show frequent patterns. Moreover, we provide users with interactive feature to explicitly show connections between superset and subsets of sets of visited web pages. Experimental results show the effectiveness of our icVAT for visual analytics of frequent patterns about web data.

In the current era of Big data, high volumes of valuable data can be generated at a high velocity from high-varieties of data sources in various real-life applications ranging from sensor networks to social networks, from bio-informatics to chemical informatics. In addition, Big data are also available in business, education, engineering, finance, healthcare, scientific, telecommunication, and transportation domains. A collection of these data can be viewed as a big dynamic graph structure. Embedded in them are implicit, previously unknown, and potentially useful knowledge. Consequently, efficient knowledge discovery algorithms for mining frequent subgraphs from these dynamic streaming graph structured data are in demand. On the one hand, some existing algorithms discover collections of frequently co-occurring edges, which may be disjoint. On the other hand, some other existing algorithms discover frequent subgraphs by requiring very large memory space. With high volumes of Big data, available memory space may be limited. To discover collections of frequently co-occurring connected edges, we present in this paper two efficient algorithms that require small memory space. Evaluation results show the efficiency of our edge-based algorithms in mining frequent subgraphs from graph streams.

Self-organizing linked lists perform well for handling requests with strong locality. Non-blocking shared data structures are robust and reliable. We present the first non-blocking self-organizing linked list algorithm which supports linearizable search, insert and remove operations. The experimental results show that our algorithm performs well for long linked lists with high percentages of search operations.