Sketching Framework Research Articles

A Systematic Literature Review of Novice Visual Representations of Design Ideas: A Three-Pronged Design Sketching Framework

ABSTRACT The purpose of this study was to systematically investigate how novice visual representation of design ideas has been operationalized, measured, or assessed in the research literature. In the different screening phases in this systematic review, inclusion, exclusion, and quality criteria were applied. From an initial sample of 958 articles, 40 studies were included in the final step of the coding process and qualitative synthesis. Applying provisional and open coding, three broad themes and 23 characteristics were identified that researchers used to conceptualize sketching of ideas in novice design activities: Communicating Ideas, Visual-Spatial Characteristics, and Design Creativity. We propose this Three-pronged Design Sketching (3-pDS) framework to examine and better understand novice design sketches, that is, K–16.

A Shifting Filter Framework for Dynamic Set Queries

Set query is a fundamental problem in computer systems. Plenty of applications rely on the query results of membership, association, and multiplicity. A traditional method that addresses such a fundamental problem is derived from Bloom filter. However, such methods may fail to support element deletion, require additional filters or apriori knowledge, making them unamenable to a high-performance implementation for dynamic set representation and query. In this paper, we envision a novel sketch framework that is multi-functional, non-parametric, space efficient, and deletable. As far as we know, none of the existing designs can guarantee such features simultaneously. To this end, we present a general shifting framework to represent auxiliary information (such as multiplicity, association) with the offset. Thereafter, we specify such design philosophy for a hash table horizontally at the slot level, as well as vertically at the bucket level. Theoretical and experimental results jointly demonstrate that our design works exceptionally well with three types of set queries under small memory.

Double-Anonymous Sketch: Achieving Top-K-fairness for Finding Global Top-K Frequent Items

Finding top-K frequent items has been a hot topic in data stream processing in recent years, which has a wide range of applications. However, most of existing sketch algorithms focuses on finding local top-K in a single data stream. In this paper, we work on finding global top-K in multiple disjoint data streams. We find that directly deploying prior sketch algorithms is often unfair under global scenarios, which will degrade the accuracy of global top-K. We define top-K-fairness and show that it is important for finding global top-K. To achieve top-K-fairness, we propose a new sketch framework, called the Double-Anonymous sketch. The process of finding global top-K items is similar to that of paper reviewing and democratic elections. In these scenarios, double-anonymity is often an effective strategy to achieve top-K-fairness. We also propose two techniques, hot panning, and early freezing, to further improve the accuracy. We theoretically prove that the Double-Anonymous sketch achieves top-K-fairnesswhile keeping high accuracy. We perform extensive experiments to verify top-K-fairness in the scenario of disjoint data streams. The experimental results show that the Double-Anonymous sketch's error is up to 129 times (60 times on average) smaller than the state-of-the-art. All the related source code is open-sourced and available at Github.

Community detection with a subsampled semidefinite program

Semidefinite programming is an important tool to tackle several problems in data science and signal processing, including clustering and community detection. However, semidefinite programs are often slow in practice, so speed up techniques such as sketching are often considered. In the context of community detection in the stochastic block model, Mixon and Xie (IEEE Trans Inform Theory 67(10): 6832–6840, 2021) have recently proposed a sketching framework in which a semidefinite program is solved only on a subsampled subgraph of the network, giving rise to significant computational savings. In this short paper, we provide a positive answer to a conjecture of Mixon and Xie about the statistical limits of this technique for the stochastic block model with two balanced communities.

Stingy sketch

Recording the frequency of items in highly skewed data streams is a fundamental and hot problem in recent years. The literature demonstrates that sketch is the most promising solution. The typical metrics to measure a sketch are accuracy and speed, but existing sketches make only trade-offs between the two dimensions. Our proposed solution is a new sketch framework called Stingy sketch with two key techniques: Bit-pinching Counter Tree ( BCTree ) and Prophet Queue ( PQueue ) which optimizes both the accuracy and speed. The key idea of BCTree is to split a large fixed-size counter into many small nodes of a tree structure, and to use a precise encoding to perform carry-in operations with low processing overhead. The key idea of PQueue is to use pipelined prefetch technique to make most memory accesses happen in L2 cache without losing precision. Importantly, the two techniques are cooperative so that Stingy sketch can improve accuracy and speed simultaneously. Extensive experimental results show that Stingy sketch is up to 50% more accurate than the SOTA of accuracy-oriented sketches and is up to 33% faster than the SOTA of speed-oriented sketches.

A Sketch Framework for Approximate Data Stream Processing in Sliding Windows

Data stream processing has become a hot issue in recent years due to the arrival of big data era. There are three fundamental stream processing tasks: membership query, frequency query and Top-K query. While most existing solutions address these queries in fixed windows, this paper focuses on a more challenging task: answering these queries in sliding windows. While most existing solutions address different kinds of queries by using different algorithms, this paper focuses on a generic framework. In this paper, we propose a generic framework, namely Sliding sketches, which can be applied to many existing solutions for the above three queries, and enable them to support queries in sliding windows. We apply our framework to five state-of-the-art sketches for the above three kinds of queries. Theoretical analysis and extensive experimental results show that after using our framework, the accuracy of existing sketches that do not support sliding windows becomes much higher than the corresponding best prior art. We released all the source code at Github.

General virtual sketching framework for vector line art

General virtual sketching framework for vector line art

General virtual sketching framework for vector line art

Vector line art plays an important role in graphic design, however, it is tedious to manually create. We introduce a general framework to produce line drawings from a wide variety of images, by learning a mapping from raster image space to vector image space. Our approach is based on a recurrent neural network that draws the lines one by one. A differentiable rasterization module allows for training with only supervised raster data. We use a dynamic window around a virtual pen while drawing lines, implemented with a proposed aligned cropping and differentiable pasting modules. Furthermore, we develop a stroke regularization loss that encourages the model to use fewer and longer strokes to simplify the resulting vector image. Ablation studies and comparisons with existing methods corroborate the efficiency of our approach which is able to generate visually better results in less computation time, while generalizing better to a diversity of images and applications.

A Sketching Framework for Reduced Data Transfer in Photon Counting Lidar

Single-photon lidar has become a prominent tool for depth imaging in recent years. At the core of the technique, the depth of a target is measured by constructing a histogram of time delays between emitted light pulses and detected photon arrivals. A major data processing bottleneck arises on the device when either the number of photons per pixel is large or the resolution of the time stamp is fine, as both the space requirement and the complexity of the image reconstruction algorithms scale with these parameters. We solve this limiting bottleneck of existing lidar techniques by sampling the characteristic function of the time of flight (ToF) model to build a compressive statistic, a so-called sketch of the time delay distribution, which is sufficient to infer the spatial distance and intensity of the object. The size of the sketch scales with the degrees of freedom of the ToF model (number of objects) and not, fundamentally, with the number of photons or the time stamp resolution. Moreover, the sketch is highly amenable for on-chip online processing. We show theoretically that the loss of information for compression is controlled and the mean squared error of the inference quickly converges towards the optimal Cram\'er-Rao bound (i.e. no loss of information) for modest sketch sizes. The proposed compressed single-photon lidar framework is tested and evaluated on real life datasets of complex scenes where it is shown that a compression rate of up-to 150 is achievable in practice without sacrificing the overall resolution of the reconstructed image.

Emerging intelligent big data analytics for cloud and edge computing

Emerging intelligent big data analytics for cloud and edge computing

SA Sketch: A self‐adaption sketch framework for high‐speed network

SummarySketch is a compact data structure used to summarize data streams. It is widely used in the measurement of network traffic, and its accuracy is higher than traditional methods. Currently, there are some typical sketches: Count‐Min Sketch, CU Sketch, and Count Sketch. According to the characteristics of network traffic, we propose a new sketch framework called Self‐Adaption Sketch, which is combined Sketch with Bloom Filter. In the framework, the sketch is created dynamically and the memory space is adjusted timely according to the network traffic by using the concept carrying. Our experiment results showed that the space utilization and accuracy are significantly improved while the throughput of self‐adaption sketch is maintained at a relatively good level.

Cartoonish sketch-based face editing in videos using identity deformation transfer

We address the problem of using hand-drawn sketches to create exaggerated deformations to faces in videos, such as enlarging the shape or modifying the position of eyes or mouth. This task is formulated as a 3D face model reconstruction and deformation problem. We first recover the facial identity and expressions from the video by fitting a face morphable model for each frame. At the same time, user’s editing intention is recognized from input sketches as a set of facial modifications. Then a novel identity deformation algorithm is proposed to transfer these facial deformations from 2D space to the 3D facial identity directly while preserving the facial expressions. After an optional stage for further refining the 3D face model, these changes are propagated to the whole video with the modified identity. Both the user study and experimental results demonstrate that our sketching framework can help users effectively edit facial identities in videos, while high consistency and fidelity are ensured at the same time.

Pyramid sketch

Sketch is a probabilistic data structure, and is used to store and query the frequency of any item in a given multiset. Due to its high memory efficiency, it has been applied to various fields in computer science, such as stream database, network traffic measurement, etc. The key metrics of sketches for data streams are accuracy, speed, and memory usage. Various sketches have been proposed, but they cannot achieve both high accuracy and high speed using limited memory, especially for skewed datasets. To address this issue, we propose a sketch framework, the Pyramid sketch, which can significantly improve accuracy as well as update and query speed. To verify the effectiveness and efficiency of our framework, we applied our framework to four typical sketches. Extensive experimental results show that the accuracy is improved up to 3.50 times, while the speed is improved up to 2.10 times. We have released our source codes at Github [1].