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About 204 papers for “Minification”

CSS Minification via Constraint Solving

Matthew Hague, Anthony W. Lin, Chih-Duo Hong · ACM Transactions on Programming Languages and Systems · 2019

Minification is a widely accepted technique that aims at reducing the size of the code transmitted over the web. This article concerns the problem of semantic-preserving minification of Cascading Style Sheets (CSS)—the de facto language for styling web documents—based on merging similar rules. The cascading nature of CSS makes the semantics of CSS files sensitive to the ordering of rules in the file. To automatically identify rule-merging opportunities that best minimise file size, we reduce the rule-merging problem to a problem concerning “CSS-graphs,” i.e., node-weighted bipartite graphs with a dependency ordering on the edges, where weights capture the number of characters. Constraint solving plays a key role in our approach. Transforming a CSS file into a CSS-graph problem requires us to extract the dependency ordering on the edges (an NP-hard problem), which requires us to solve the selector intersection problem. To this end, we provide the first full formalisation of CSS3 selectors (the most stable version of CSS) and reduce their selector intersection problem to satisfiability of quantifier-free integer linear arithmetic, for which highly optimised SMT-solvers are available. To solve the above NP-hard graph optimisation problem, we show how Max-SAT solvers can be effectively employed. We have implemented our rule-merging algorithm and tested it against approximately 70 real-world examples (including examples from each of the top 20 most popular websites). We also used our benchmarks to compare our tool against six well-known minifiers (which implement other optimisations). Our experiments suggest that our tool produced larger savings. A substantially better minification rate was shown when our tool is used together with these minifiers.

Cited by 5

Apparent minification in an imaging display

Meehan., James W. R. · Monash University · 2021

This thesis was scanned from the print manuscript for digital preservation and is copyright the author. Researchers can access this thesis by asking their local university, institution or public library to make a request on their behalf. Monash staff and postgraduate students can use the link in the References field.

Cited by 0Open access

Minification processes with discrete marginals

Vipul Kalamkar · Journal of Applied Probability · 1995

We investigate the stationarity of minification processes when the marginal is a discrete distribution. There is a close relationship between the problem considered by Arnold and Isaacson (1976) and the stationarity in minification processes. We give a necessary and sufficient condition for a discrete distribution to be the marginal of a stationary minification process. Members of the Poisson and negative binomial families can be the marginals of stationary minification processes. The geometric minification process is studied in detail, and two characterizations of it based on the structure of the innovation process are given.

Cited by 7

Code stylometry vs formatting and minification

Stefano Balla, Maurizio Gabbrielli, Stefano Zacchiroli · PeerJ Computer Science · 2024

The automatic identification of code authors based on their programming styles—known as authorship attribution or code stylometry—has become possible in recent years thanks to improvements in machine learning-based techniques for author recognition. Once feasible at scale, code stylometry can be used for well-intended or malevolent activities, including: identifying the most expert coworker on a piece of code (if authorship information goes missing); fingerprinting open source developers to pitch them unsolicited job offers; de-anonymizing developers of illegal software to pursue them. Depending on their respective goals, stakeholders have an interest in making code stylometry either more or less effective. To inform these decisions we investigate how the accuracy of code stylometry is impacted by two common software development activities: code formatting and code minification. We perform code stylometry on Python code from the Google Code Jam dataset (59 authors) using a code2vec-based author classifier on concrete syntax tree (CST) representations of input source files. We conduct the experiment using both CSTs and ASTs (abstract syntax trees). We compare the respective classification accuracies on: (1) the original dataset, (2) the dataset formatted with Black, and (3) the dataset minified with Python Minifier. Our results show that: (1) CST-based stylometry performs better than AST-based (51.00%→68%), (2) code formatting makes a significant dent (15%) in code stylometry accuracy (68%→53%), with minification subtracting a further 3% (68%→50%). While the accuracy reduction is significant for both code formatting and minification, neither is enough to make developers non-recognizable via code stylometry.

Cited by 1Open access

Apparent minification in an imaging display

Meehan., James W. R. · Figshare · 2021

This thesis was scanned from the print manuscript for digital preservation and is copyright the author. Researchers can access this thesis by asking their local university, institution or public library to make a request on their behalf. Monash staff and postgraduate students can use the link in the References field.

Cited by 0Open access

Development of a microSPECT system using minification

Carmen Bouckaert, Stefaan Vandenberghe, Roel Van Holen · Ghent University Academic Bibliography (Ghent University) · 2012

tomography*(SPECT)*is*a*nuclear*imaging*technique*that*allows* to* make* a* functional* image* of* the* body* in* vivo,* in* contrast* to* the* wellFknown* radiography* techniques*that*generate*anatomical*images*of*the*body.*This*is*achieved*by*injecting*a*substance*labeled*with*a*radioactive*isotope*in*the*body*that*will*be*directed*towards*certain*regions*in*the* body.*The*amount*and*regions*of*uptake*can*then*give*information*about*the*presence*of*tumors*or*the*functioning*of*certain*organs*in*the*body.**For* small* animal* imaging,* most* SPECT* systems* use* pinhole* collimators.*This* pinhole* acts* like* a* camera*obscura*by*magnifying*the*image*on*the*detector*and*thus*allowing*to*overcome*the*low* intrinsic* resolution* of* the* detector.*Although* a* high* resolution* can* be* achieved* using* this* technique,*it*results*in*a*very*low*sensitivity*and*therefore*long*scan

Cited by 0Open access

Minification influences spatial judgments in virtual environments

Scott Kuhl, William B. Thompson, Sarah H. Creem-Regehr · 2006

Distances in immersive virtual environments (VEs) have been commonly reported as being spatially compressed while the same judgments are performed accurately in real space. Previous research has been unable to determine the cause of this spatial compression in VEs. The work reported here seeks to improve spatial judgments in VEs by manipulating the computer graphics in a way that biases distance judgments in a controlled manner, but is unnoticed by VE users. We show that shrinking the displayed image and appropriately rendering the scene to fill in the space resulting from shrinking the image ("minification") causes people to make more accurate distance judgments than they do with normally rendered graphics. Possible explanations for this effect include changes to the visual angle of declination from horizon to targets and changes in familiar size.

Cited by 38

Minification and gap affordances in head-mounted displays

James W. Walker, Ruimin Zhang, Scott Kuhl · 2012

Numerous studies report that people underestimate egocentric distances in head-mounted display (HMD) virtual environments compared to real environments when measured with direct blind walking. Recently, Zhang et al. [2012] demonstrated that minification can make verbal reports and blind walking more consistent with similar real world judgments. This research examines if minification causes people to misjudge gap affordances. We found that minification did not affect participants' verbal reports of whether they could fit through a gap without turning their shoulders.

Cited by 4

Code Stylometry vs Formatting and Minification - Replication Package

BALLA, STEFANO, GABBRIELLI, MAURIZIO, ZACCHIROLI, STEFANO · Zenodo (CERN European Organization for Nuclear Research) · 2024

Setup Before you proceed with any of the steps, ensure you have Python installed. Step 1: Set up the Python environment. Use the requirements.txt file to install the necessary packages. python -m venv venvsource venv/bin/activate # On Windows, use: venv\Scripts\activatepip install -r requirements.txt 2. Dataset Creation To create the dataset, navigate to the dataset_creation directory and execute the Jupyter Notebook titled dataset_notebook.ipynb. cd dataset_creationjupyter notebook dataset_notebook.ipynb Follow the instructions inside the notebook to generate the dataset required for training. 3. Training the Neural Network The neural network is trained using the run.py file located in the NeuralNetwork directory. Note: Training can be resource-intensive. In our experiments, we utilized the SLURM job scheduler for this purpose. Due to the high resource demand, we haven't provided a training example within the network_notebook.ipynb. However, you can examine a sample model and understand the training procedure by looking through the provided file.

Cited by 0Open access

Pseudophakes Experience Apparent Minification in an Imaging Display

James W. Meehan, George Smith, Ross H. Day · Perception · 1994

Apparent minification of virtual images has been attributed to accommodation micropsia, that is, a reduction in the apparent size of viewed objects accompanying an inward accommodation of the eyes. Thirteen bilateral pseudophakes incapable of accommodation but with good residual vision were tested monocularly for apparent minification in the viewfinder of a single-lens reflex camera. Apparent minification of about the same order as that typically found with normals occurred with the pseudophakic observers, demonstrating that pseudophakes are subject to distortions in apparent size with virtual-imaging displays in the same way as normals, thus establishing that visual accommodation is not a necessary condition for the effect.

Cited by 4

Code Stylometry vs Formatting and Minification - Replication Package

BALLA, STEFANO, GABBRIELLI, MAURIZIO, ZACCHIROLI, STEFANO · Zenodo (CERN European Organization for Nuclear Research) · 2024

Setup Before you proceed with any of the steps, ensure you have Python installed. Step 1: Set up the Python environment. Use the requirements.txt file to install the necessary packages. python -m venv venvsource venv/bin/activate # On Windows, use: venv\Scripts\activatepip install -r requirements.txt 2. Dataset Creation To create the dataset, navigate to the dataset_creation directory and execute the Jupyter Notebook titled dataset_notebook.ipynb. cd dataset_creationjupyter notebook dataset_notebook.ipynb Follow the instructions inside the notebook to generate the dataset required for training. 3. Training the Neural Network The neural network is trained using the run.py file located in the NeuralNetwork directory. Note: Training can be resource-intensive. In our experiments, we utilized the SLURM job scheduler for this purpose. Due to the high resource demand, we haven't provided a training example within the network_notebook.ipynb. However, you can examine a sample model and understand the training procedure by looking through the provided file.

Cited by 0Open access

A note on the characterization ofsome minification processes

Wiesław Dziubdziela · Applicationes Mathematicae · 1997

We present a stochastic model which yields a stationary Markov process whose invariant distribution is maximum stable with respect to the geometrically distributed sample size. In particular, we obtain the autoregressive Pareto processes and the autoregre

Cited by 0Open access