DRAM-based cache is a practical approach to enhancing the performance of large-capacity SSDs. Due to DRAM’s limited capacity, cache sizes are significantly smaller than the data scale of workloads – and cache replacement schemes determine the cache hit ratio and SSD performance when a cache reaches full capacity. Prior work embarked on leveraging machine learning models (ML) to predict future access patterns in workloads, aiding cache replacement decisions for intelligent cache schemes inside SSDs. Unfortunately, existing ML-based cache schemes often overlook the impacts of data granularity – including page, request, and coarse granularities – of training datasets, model inference time overhead, and computational overhead on SSD performance. To address these challenges, we are motivated to propose an sample-inference-based intelligentcachescheme – eCache. eCache accurately predicts the future reuse distance of each sampled requested pages, which can enhance the accuracy of decision-making for the cache replacement. In particular, we design a parallel framework to curb the time overhead caused by model inference. We advocate for a random-group sampling inference method that utilizes the most accurate model while reducing computational overhead. Moreover, we implement eCache on the state-of-the-art SSD simulator, MQSim, and compare it against alternative cache schemes (i.e., CCache, NCache, LAC, and VS-batch). The experimental results unveil that compared with the other cache schemes, eCache significantly reduces the average response time by up to 79.68% with an average reduction of 44.08%. When compared with the page-granularity-ML-empowered cache schemes, eCache greatly curtails computational overhead by up to 85.23% with an average reduction of 66.00%.

Interior scene colorization is vastly demanded in areas such as personalized architecture design. Existing works either require manual efforts to colorize individual objects or conform to fixed color patterns automatically learned from prior knowledge, whilst neglecting user preference. Quantitatively identifying user preferences is challenging, particularly at the early stage of the design process. The 3D setup also presents new challenges as the inhabitant can observe from any possible viewpoint. We propose a representative view selection method based on visual attention and a progressive preference inference model. We particularly focus on the progressive integration of eye-tracked user preference, which enables the assistance in creativity support and allows the possibility of convergent thinking. A series of user studies have been conducted to validate the effectiveness of the proposed view selection method, preference inference model and the creativity support mechanism.

Color design for 3D indoor scenes is a challenging problem due to many factors that need to be balanced. Although learning from images is a commonly adopted strategy, this strategy may be more suitable for natural scenes in which objects tend to have relatively fixed colors. For interior scenes consisting mostly of man-made objects, creative yet reasonable color assignments are expected. We propose$C^{3}$C3Assignment, a system providing diverse suggestions for interior color design while satisfying general global and local rules including color compatibility, color mood, contrast, and user preference. We extend these constraints from the image domain to$\mathbb {R}^3$, and formulate 3D interior color design as an optimization problem. The design is accomplished in an omnidirectional manner to ensure a comfortable experience when the inhabitant observes the interior scene from possible positions and directions. We design a surrogate-assisted evolutionary algorithm to efficiently solve the highly nonlinear optimization problem for interactive applications, and investigate the system performance concerning problem complexity, solver convergence, and suggestion diversity. Preliminary user studies have been conducted to validate the rule extension from 2D to 3D and to verify system usability.

Interacting with out of reach or occluded VR objects can be cumbersome. Although users can change their position and orientation, such as via teleporting, to help observe and select, doing so frequently may cause loss of spatial orientation or motion sickness. We present vMirror, an interactive widget leveraging reflection of mirrors to observe and select distant or occluded objects. We first designed interaction techniques for placing mirrors and interacting with objects through mirrors. We then conducted a formative study to explore a semi-automated mirror placement method with manual adjustments. Next, we conducted a target-selection experiment to measure the effect of the mirror’s orientation on users’ performance. Results showed that vMirror can be as efficient as direct target selection for most mirror orientations. We further compared vMirror with teleport technique in a virtual treasure hunt game and measured participants’ task performance and subjective experiences. Finally, we discuss vMirorr user experience and present future directions.