Information seeking on mobile devices is often fragmented, trapping users in repetitive cycles of context switching and data re-entry, which increases cognitive load and disrupts workflow. Existing mobile agents provide limited cross-source integration and are largely opaque, presenting progress as a linear feed with few opportunities to intervene, steer, or take control. We present DroidRetriever, a transparent, steerable system for cross-source mobile information seeking. It accepts voice or typed input and the multi-LLM system decomposes the task, navigates to target pages, takes screenshots, and synthesizes a concise report with citation-linked screenshots. We make the process transparent through a progress dashboard combining sub-task progress and real-time exploration maps for seamless takeover. DroidRetriever also pauses on detected privacy or high-risk screens and prompts intervention. Across 35 tasks over 24 apps, experiments and user studies demonstrate improvements in coverage, transparency, and reduced workload. We release our code at https://github.com/AkimotoAyako/DroidRetriever.

Immersive technologies are increasingly applied in therapeutic and well-being practices, yet most AR systems focus on dyadic client–avatar interactions and overlook richer therapeutic structures that involve therapists. We introduce AReframedChair, an AR system that reimagines the traditional Empty Chair technique by enabling self-dialogue with a personalized avatar representing one’s past or future self. In a between-subjects study with 60 adults, we compared the traditional Empty Chair method with two AR-reframed modes: Dyadic (client–avatar) and Triadic (client–avatar– therapist). Participants’ survey responses showed that the Dyadic mode elicited greater positive affect and self-compassion in the past-self scenarios, whereas the Triadic mode produced stronger gains in motivation and reflections in future-self scenarios. Thematic analysis further revealed distinct roles: the Avatar facilitated emotional entry, reassurance, and cognitive reframing, while the Therapists intervened at critical moments to down-regulate intensity, redirect attention, and enhance reflection. These findings open up new design pathways for mental health technologies.

Enhancing the intelligence of smart systems, such as smart homes, smart vehicles, and smart grids, critically depends on developing sophisticated planning capabilities that can anticipate the next desired function based on historical interactions. While existing methods view user behaviors as sequential data and apply models like RNNs and Transformers to predict future actions, they often fail to incorporate domain knowledge and capture personalized user preferences. In this paper, we propose a novel approach that incorporates LLM-enhanced logs and personalized prompts. Our approach first constructs a graph that captures individual behavior preferences derived from their interaction histories. This graph effectively transforms into a soft continuous prompt that precedes the sequence of user behaviors. Then our approach leverages the vast general knowledge and robust reasoning capabilities of a pretrained LLM to enrich the oversimplified and incomplete log records. By enhancing these logs semantically, our approach better understands the user's actions and intentions, especially for those rare events in the dataset. We evaluate the method across four real-world datasets from both smart vehicle and smart home settings. The findings validate the effectiveness of our LLM-enhanced description and personalized prompt, shedding light on potential ways to advance the intelligence of smart space.

Solid-state drives (SSDs) are massively deployed in various fields, especially in data centers, for their excellent cost-effectiveness. However, SSDs may fail due to their imperfect manufacturing processes, resulting in system-level failures and even downtime in data centers. This makes SSD failure prediction critical. Current studies focus on dealing with data missing, numerical normalization, and other statistical issues in using machine learning methods, but the consideration of the reliability characteristics of the underlying flash media of SSDs and the timeliness (time duration between predicted failure and real failure) of SSD failure prediction result is missing.In this work, we study the failure characteristics of over 200,000 drives from industry data centers over a 4-year period, as well as daily data. The relationship between SSD attribute values and failures is first investigated. Then, we analyzed the SSD failure characteristics from several aspects (causes, differences between failures, and timeliness of prediction results) relying on flash reliability characteristics. Based on these, a novel SSD failure prediction method (Prophet) is proposed. Specifically, Prophet contains the following two components. First, to cope with the differences between failures, a diff-state method is proposed for differential machine learning modeling of SSDs in different “States”. We define the “State” of an SSD, which represents the range of values in which the SSD currently lies in terms of some key attributes. Through flash reliability characteristics, we distinguish between different failures before training the model to obtain accurate predictions of different failure behaviors. Second, a recovery period method is proposed to enhance the timeliness of SSD failure prediction result by designing the sample selection method. The enhanced timeliness can be utilized by operations personnel to handle failed SSDs, such as replacement and repair. The evaluation results of the real dataset show that the predictive ability of Prophet is improved amazingly, realizing a high recall and low false-positive rates while providing sufficient response time for the processing of failed SSDs.

3D NAND high-density flash memory is widely used in edge computing, IoT, and automotive applications due to its high performance, low latency, and low storage cost characteristics. These scenarios require operation in extreme temperature environments, with cross-temperature read/write occurring frequently. However, cross-temperature affects programming reliability, leading to high raw bit error rates (RBER), which degrades read performance and increases energy consumption. In this paper, we propose a novel temperature-aware differential programming (TADP) scheme to optimize read performance and energy consumption under cross-temperature read/write. Specifically, first, a temperature-aware compensatory programming scheme is proposed to reduce the cross-temperature-induced degradation of RBER. Second, a layer variation-aware compensatory programming scheme is proposed to reduce the compensatory programming latency. Finally, a degraded programming scheme is proposed to enhance the temperature toughness of poorly temperature-tough word-lines by using them as MLC. Evaluated on 233-layer 3D triple-level-cell (TLC) NAND flash, TADP achieved encouraging optimizations in programming reliability, energy consumption, and read performance with minimal capacity loss.

Three-dimensionalnandflash memory using the advanced multibit-per-cell technique is widely adopted due to its high density. However, it faces the problem of deteriorating read performance and energy consumption due to decreased reliability. Low-density parity-check code (LDPC) is typically adopted as an error correction code (ECC) to encode data and provide fault tolerance. To reduce the cost, LDPC with a high code rate is always adopted. However, LDPC will lead to read retry operations when the accessed data are not successfully decoded, and such retry-induced performance degradation is serious, especially for modern high-density flash memory. In this work, a reliability-aware differential ECC (READECC) approach is proposed to reduce redundancy protection and storage cost of LDPC with a low code rate and optimize the read performance. The basic idea is to adopt LDPC with a suitable code rate considering both data access characteristics and flash reliability characteristics. First, hot reads are identified based on the frequency of being accessed. Second, based on the reliability variation characteristics, the life of flash memory is divided into three reliability periods. As the reliability period shifts, the code rate of the LDPC adjusts adaptively to minimize redundancy protection. Third, an adaptive-sized logical page approach is further proposed to support LDPC with strong error correction capability (a low code rate) with a low storage cost. Through careful design and evaluation on 3-D triple-level-cellnandflash memory, READECC achieves encouraging optimizations with a negligible cost.

To address critical challenges in effectively identifying user intent and forming relevant information presentations and recommendations in VR environments, we propose an innovative condition-based multi-modal human-AI cooperation framework. It highlights the intent tuples (intent, condition, intent prompt, action prompt) and 2-Large-Language-Models (2-LLMs) architecture. This design, utilizes “condition” as the core to describe tasks, dynamically match user interactions with intentions, and empower generations of various tailored multi-modal AI responses. The architecture of 2-LLMs separates the roles of intent detection and action generation, decreasing the prompt length and helping with generating appropriate responses. We implemented a VR-based intelligent furniture purchasing system based on the proposed framework and conducted a three-phase comparative user study. The results conclusively demonstrate the system’s superiority in time efficiency and accuracy, intention conveyance improvements, effective product acquisitions, and user satisfaction and cooperation preference. Our framework provides a promising approach towards personalized and efficient user experiences in VR.

Modern mobile devices adopt two-level memory swapping consisting of ZRAM and storage devices to relieve memory pressure. In the swap subsystem, ZRAM can improve application responsiveness and reduce write traffic to storage devices while consuming physical memory and additional CPU cycles. To better utilize ZRAM and improve system performance, we propose ElasticZRAM, an elastic ZRAM to redesign the traditional memory swapping with full awareness of the characteristics of applications and NAND flash-based storage devices on mobile devices. Experimental results on Google Pixel 6 demonstrate that ElasticZRAM improves application response time by up to 24.8% with negligible overhead compared with state-of-the-arts.

High-densitysolid-state drives (SSDs), such as triple-level cell (TLC) or quad-level cell (QLC) flash, are adopted in parity-based RAID systems to achieve high reliability with low redundancy. However, the parity writes cause high write wear, which is unfriendly to such high-density SSDs with low write endurance. Conversely, high-performance SSDs, such as ZNAND, XL-Flash, have high write endurance but their high cost per bit hinders their deployment in RAID. This paper proposed a novel hybrid RAID structure, RAID45, to reduce parity writes for highdensity SSDs. Specifically, RAID45 uses high-performance SSD to store the parity of write-intensive stripes to absorb as much of the wear of parity writes on high-density SSDs as possible. Experimental results on real platform show that RAID45 achieves encouraging parity write reduction on the high-density SSDs.

Mobile devices always cache numerous files during application runtime, which can be trimmed to improve the user experience. However, existing cache file trimming methods are unaware of the cleaning cost within the file system and storage devices, which degrades the system performance and storage lifetime, resulting in low benefits of trimming cache files. Motivated by this, an adaptive cache file trimming (CacheTrimmer) scheme is proposed to trim cache files for performance and lifetime improvement. The basic idea is to determine the trimming timing based on the cleaning cost of the file system and storage device, maximizing the benefit of trimming cache files. Specifically, CacheTrimmer includes two components: First, a cleaning cost-aware trimming method is proposed to trim cache files by recording the index information of cache files in a list and determining the timing and size of file trimming. Second, to avoid trimming-induced intra-segment fragmentation and improve trimming efficiency, a log-structured cache scheme is further proposed to maintain the cache files in separate segments. We prototype CacheTrimmer with a real mobile platform. Experimental results under real workloads show that CacheTrimmer achieves encourage performance and lifetime improvement compared to the state-of-the-art.

F2FS improves performance and longevity through out-place updates, and is now wildly used in the real world. However, additional overhead is introduced to support such design, which leads to a significant performance bottleneck when performing fsync(). Through a series of experimental observations, the paper reveals the impact of dnode page writeback on throughput and fsync() latency. The serial flushing of data pages and dnode pages in the current fsync() design limits the potential for parallel write back and fails to fully utilize the parallelism of flash devices. We then deeply look into the current fsync() design and find the main difficulty of paralleling fsync() is the dependency between dnode pages and data pages. Based on these findings, we propose a new dual-thread design that significantly reduces the total latency of fsync() and improves throughput by pre-allocating data pages. Then, we give two optimizations to reduce overhead. A red-black tree is introduced to cache old block addresses for better node management performance. A linked list is introduced to avoid contention of the page cache for better page performance. We implemented our method in Linux Kernel, and the experimental results show that our dual-thread design can decrease fsync() latency and increase write throughput in different situations.

In human-AI collaborative tasks, the distribution of human behavior, influenced by mental models, is non-stationary, manifesting in various levels of initiative and different collaborative strategies. A significant challenge in human-AI collaboration is determining how to collaborate effectively with humans exhibiting non-stationary dynamics. Current collaborative agents involve initially running self-play (SP) multiple times to build a policy pool, followed by training the final adaptive policy against this pool. These agents themselves are a single policy network, which is $\textbf{insufficient for handling non-stationary human dynamics}$. We discern that despite the inherent diversity in human behaviors, the $\textbf{underlying meta-tasks within specific collaborative contexts tend to be strikingly similar}$. Accordingly, we propose $\textbf{C}$ollaborative $\textbf{B}$ayesian $\textbf{P}$olicy $\textbf{R}$euse ($\textbf{CBPR}$), a novel Bayesian-based framework that $\textbf{adaptively selects optimal collaborative policies matching the current meta-task from multiple policy networks}$ instead of just selecting actions relying on a single policy network. We provide theoretical guarantees for CBPR's rapid convergence to the optimal policy once human partners alter their policies. This framework shifts from directly modeling human behavior to identifying various meta-tasks that support human decision-making and training meta-task playing (MTP) agents tailored to enhance collaboration. Our method undergoes rigorous testing in a well-recognized collaborative cooking simulator, $\textit{Overcooked}$. Both empirical results and user studies demonstrate CBPR's superior competitiveness compared to existing baselines.

Mobile task automation is an emerging field that leverages AI to streamline and optimize the execution of routine tasks on mobile devices, thereby enhancing efficiency and productivity. Traditional methods, such as Programming By Demonstration (PBD), are limited due to their dependence on predefined tasks and susceptibility to app updates. Recent advancements have utilized the view hierarchy to collect UI information and employed Large Language Models (LLM) to enhance task automation. However, view hierarchies have accessibility issues and face potential problems like missing object descriptions or misaligned structures. This paper introduces VisionTasker, a two-stage framework combining vision-based UI understanding and LLM task planning, for mobile task automation in a step-by-step manner. VisionTasker firstly converts a UI screenshot into natural language interpretations using a vision-based UI understanding approach, eliminating the need for view hierarchies. Secondly, it adopts a step-by-step task planning method, presenting one interface at a time to the LLM. The LLM then identifies relevant elements within the interface and determines the next action, enhancing accuracy and practicality. Extensive experiments show that VisionTasker outperforms previous methods, providing effective UI representations across four datasets. Additionally, in automating 147 real-world tasks on an Android smartphone, VisionTasker demonstrates advantages over humans in tasks where humans show unfamiliarity and shows significant improvements when integrated with the PBD mechanism. VisionTasker is open-source and available at https://github.com/AkimotoAyako/VisionTasker.

Sharing digital resources is a common practice in both work and personal life. Yet, sharing identical credentials, such as passwords or physical cards, not only poses significant security risks but also falls short in addressing the specific requirements of small local groups, such as parental controls, tracking user modifications, and easily updating access. To address this, we suggest a touch behavior-based method tailored for sharing in small local groups, designed to balance between ensuring relaxed security and maintaining practical functionality. Our approach aims to concurrently identify in-group users and detect out-of-group imposters. Specifically, our approach extracts effective identity representations that are robust to in-group variability and out-of-group uncertainty by learning a pair of touch-behavioral and within-group similarity embeddings. While the former captures the unique features of user touch characteristics, the latter reflects the typical group-wide similarity structure that an in-group user is expected to possess from a holistic perspective. Experimental results showcase the effectiveness of our method even with few samples for training. It maintains accuracy despite the group growing larger and shows resilience against the advanced attacks. This offers a promising way to keep group access both user-friendly and relatively secure, striking a crucial balance for small groups’ needs.

Memory swapping ensures smooth application switching for mobile systems by caching applications in the background. To further play the role of memory swapping, remote swapping across mobile devices has been widely studied, which caches applications to nearby remote devices by remote paging. However, due to the massive remote I/Os and unguaranteed swap throughput, the current remote swapping is limited with an unsatisfactory user experience, especially under variable network conditions. This paper first studies the access characteristics of applications and clarifies the impact of various network traffic on remote swapping. Motivated by these, an efficient access characteristic-guided remote swapping framework (ACR-Swap + ) is proposed to optimize remote swapping across mobile devices with resilient remote paging. ACR-Swap + first performs selective remote paging based on the swap-in frequency of different processes and then prefetches data across devices based on the process running states. Finally, it conducts hierarchical remote paging to avoid the impact of network traffic on remote swapping. Evaluations on Google Pixel 6 show that ACR-Swap + reduces the application switching latency by 21.6% and achieves a negligible performance fluctuation under various network traffic compared to the state of the art.

Hybrid solid state drives (SSDs) that integrate high-performance and large-capacity flash are widely used due to their cost-effectiveness. The TRIM command, which is a popular command in normal SSDs to improve performance and endurance, is also recommended in hybrid SSDs. However, employing TRIM on hybrid SSDs as on normal SSDs will induce performance loss and sub-optimal endurance due to the different characteristics of flash in hybrid SSDs. To solve the problem, this paper first explores the critical factors of issuing TRIM commands to different flash. Then, this paper proposed a differential TRIM method (dTRIM), which suggests performing early TRIM on high-performance flash and lazy TRIM on high-capacity flash. Specifically, early TRIM will minimize garbage collection costs while lazy TRIM tries to avoid conflicting user requests. Experimental results demonstrate that dTRIM can significantly improve the performance and endurance of hybrid SSDs compared with the state-of-the-arts.

With cost reduction and density optimization, high-density NAND flash memory has been widely deployed in data centers and consumer devices. However, this trend has significantly degraded the read performance and lifetime of high-density NAND flash memory during the last decade. Previous works proposed to optimize flash lifetime with wear leveling (WL) and optimize read performance with reliability improvement. Although WL can improve flash lifetime, it leads to the reliability of all blocks in 3-D NAND flash decreasing simultaneously. The reliability and read performance will be degraded with flash wearing. To solve this problem, an adaptive differential wearing (ADWR) scheme is proposed to optimize the read performance and lifetime in this work. The basic idea of ADWR is to determine the size of the high-reliability area to serve hot reads based on workload characteristics. Specifically, first, a differential wearing scheme is proposed to construct different reliability areas based on the characteristics of the data. Second, a lifetime model is constructed for the ADWR to clarify the lifetime impact. Based on this, a lifetime optimization scheme is proposed to improve the flash lifetime. Finally, a differential refresh scheme is proposed to reduce the impact of read disturbance on read performance. The experiments on real-life workloads show that ADWR achieves encouraging read performance optimization with negligible impacts on the lifetime of 3-D TLC NAND flash memory.

3D NAND flash memory with advanced multi-level-cell technology has been widely adopted due to its high density, but with significantly degraded reliability. To solve the reliability issue, flash memory often adopts the low-density parity-check code (LDPC) as error correction code (ECC) to encode data and provide fault tolerance. For LDPC with a low code rate, it can provide a strong correction capability, but with a high energy cost. To avoid the cost, LDPC with a higher code rate is always adopted. When the accessed data is not successfully decoded, LDPC will rely on read retry operations to improve the error correction capability. However, the read retry operation will induce degraded read performance. In this work, a differential ECC (DECC) method is proposed to improve the read performance. The basic idea of DECC is to adopt LDPC with different code rates for data with different access characteristics. Specifically, when data is hot read and retried due to reliability, LDPC with a low code rate will be adopted to optimize performance. With this approach, the cost from LDPC with a low code rate is minimized and the performance is optimized. Through careful design and real-world workloads evaluation on a 3D triple-level-cell (TLC) NAND flash memory, DECC achieves encouraging read performance optimization.

The existing work on task assignment of human-AI cooperation did not consider the differences between individual team members regarding their capabilities, leading to sub-optimal task completion results. In this work, we propose a capability-aware shared mental model (CASMM) with the components of task grouping and negotiation, which utilize tuples to break down tasks into sets of scenarios relating to difficulties and then dynamically merge the task grouping ideas raised by human and AI through negotiation. We implement a prototype system and a 3-phase user study for the proof of concept via an image labeling task. The result shows building CASMM boosts the accuracy and time efficiency significantly through forming the task assignment close to real capabilities within few iterations. It helps users better understand the capability of AI and themselves. Our method has the potential to generalize to other scenarios such as medical diagnoses and automatic driving in facilitating better human-AI cooperation.

Compression-based schemes have been widely studied to improve the lifetime and performance of solid-state drives (SSDs). Recently, the most popular flash-friendly file system (F2FS) started supporting compression to maximize the lifetime of NAND flash-based storage. Also, compression-based computational SSDs (CSDs) are developed due to their high performance, transparency, and easy adoption. This paper will first study the compression of F2FS and CSD to understand their features. Then, cooperative compression (COCO) is proposed to optimize performance and power consumption based on the combination of F2FS and CSD. Experiments on real devices show that COCO has encouraged optimization.

QLC (4-bit-per-cell) and more-bit-per-cell 3D NAND flash memories are increasingly adopted in large storage systems. While achieving significant cost reduction, these memories face degraded performance and reliability issues. The industry has adopted two-step programming (TSP), rather than one-step programming, to perform fine-granularity program control and choose gray-code encoding, as well as LDPC (Low-Density Parity-Check Code) for error correction. Different flash manufacturers often integrate different gray-codes in their products, which exhibit different performance and reliability characteristics. Unfortunately, a fixed gray-code encoding design lacks the ability to meet the dynamic read and program performance requirements at both application and device levels.In this paper, we propose MGC, a multiple-gray-code encoding strategy, that adaptively chooses the best gray-code to meet the optimization goals at runtime. In particular, MGC first extracts the performance and reliability requirements based on application-level access patterns and detects the reliability degree of SSD. It then determines the appropriate gray-code to encode the data, either from host/user application or due to garbage collection, before writing the pages to the flash memory. MGC is integrated in FTL (flash translation layer) and enhances the flash controller to enable runtime gray-code arbitration. We evaluate the proposed MGC scheme. The results show that MGC achieves better performance and lifetime guarantee compared with state-of-the-arts and introduces little overhead.

nand flash-based solid-state drives (SSDs) are a kind of widely adopted storage. However, state-of-the-art works presented that the SSD always suffers from significant read performance degradation. One of the most critical reasons is access interference between read and write operations. This is because the read and write latency gaps are more pronounced for the latest nand flash in SSDs. In this article, an interference reduction scheme is proposed to improve performance. This is motivated by the observation from several server workloads, where read and write operations can be easily separated based on access characteristics. Considering that SSDs are always organized with many parallel units (PUs), the basic idea of this work is to partition the PUs of the SSD into different areas and place data in the corresponding area according to access characteristics. Then, the interference can be optimized by issuing read and write requests to the different areas. To realize the above design, several approaches are proposed: first, an access characteristic-based data placement and migration method is proposed for read and write request separation. Second, to further adapt the parallel requirement for different workloads, a workload-based partitioning scheme is proposed to determine the number of PUs for read and write areas. Finally, based on partitioned SSD, a hot-data driven wear-leveling method is further proposed to balance the wearing of PUs in read and write areas. Experimental results show that partitioned SSD can significantly improve the read performance and wear leveling of partitioned SSD can guarantee performance and lifetime.

With the cost reduction and density optimization, the read performance and lifetime of high-density NAND flash memory have been significantly degraded during the last decade. Previous works proposed to optimize lifetime with wear leveling and optimize read performance with reliability improvement. However, with wearing, the reliability and read performance will be degraded along with the life of the device. To solve this problem, a differential wearing scheme (DWR) is proposed to optimize the read performance. The basic idea of DWR is to partition the flash memory into two areas and wear them at different speeds. For the area with low wearing speed, read operations are scheduled for read performance optimization. For the area with high wearing speed, write operations are scheduled but designed to avoid generating bad blocks early. Through careful design and real workloads evaluation on 3D TLC NAND flash, DWR achieves encouraging read performance optimization with negligible impacts to the lifetime.

With the proliferation of IoT devices, there are growing concerns about being sensed or monitored by these devices unawares, especially in places perceived as private. We explore the design space of IoT locators to help people physically find nearby IoT devices. We first conducted a survey to understand people's willingness, current practices, and challenges in finding IoT devices. Our survey findings motivated us to design and implement low-cost locators (visual, auditory, and contextualized pictures) to help people find nearby devices. Through an iterative design process and two rounds of experiments, we found that these locators greatly reduced people's search time over a baseline of no locators. Many participants found the visual and auditory locators enjoyable. Some participants also appropriated the use of our system for other purposes, e.g., to learn about new IoT devices, instead of for privacy awareness.

Work is being digitized across all sectors, and digital account sharing has become common in the workplace. In this paper, we conduct a qualitative and quantitative study of digital account sharing practices in the workplace. Across two surveys, we examine the sharing process at work, probing what accounts people share, how and why they share those accounts, and identifying the major challenges people face in sharing accounts. Our results demonstrate that account sharing in the modern workplace serves as a norm rather than a simple workaround; centralizing collaborative activity and reducing boundary management effort are key motivations for sharing. But people still struggle with a lack of activity accountability and awareness, conflicts over simultaneous access, difficulties controlling access, and collaborative password use. Our work provides insights into the current difficulties people face in workplace collaboration with online account sharing, as a result of inappropriate designs that still assume a single-user model for accounts. We highlight opportunities for CSCW and HCI researchers and designers to better support sharing by multiple people in a more usable and secure way.

In this paper we present a simple and reliable authentication method for mobile devices equipped with multi-touch screens such as smart phones, tablets and laptops. Users are authenticated by performing specially designed multi-touch gestures with one swipe on the touchscreen. During this process, both hand geometry and behavioral characteristics are recorded in the multi-touch traces and used for authentication. By combining both geometry information and behavioral characteristics, we overcome the problem of behavioral variability plaguing many behavior based authentication techniques - which often leads to less accurate authentication or poor user experience - while also ensuring the discernibility of different users with possibly similar handshapes. We evaluate the design of the proposed authentication method thoroughly using a large multi-touch dataset collected from 161 subjects with an elaborately designed procedure to capture behavior variability. The results demonstrate that the fusion of behavioral information with hand geometry features produces effective resistance to behavioral variability over time while at the same time retains discernibility. Our approach achieves EER of 5.84% with only 5 training samples and the performance is further improved to EER of 1.88% with enough training. Security analyses are also conducted to demonstrate that the proposed method is resilient against common smartphone authentication threats such as smudge attack, shoulder surfing attack and statistical attack. Finally, user acceptance of the method is illustrated via a usability study.