J. S. Marron

76 papers A* 1B 4Journal 57Unranked 13
YearRankTypeTitle / Venue / Authors
2025 J jnl
J. Math. Imaging Vis.
Stephen M. Pizer, Zhiyuan Liu, Junjie Zhao, Nicholas Tapp-Hughes, James N. Damon, Miaomiao Zhang, J. S. Marron, Mohsen Taheri, Jared Vicory
2025 J jnl
J. Comput. Graph. Stat.
Thomas H. Keefe, J. S. Marron
2025 J jnl
Bioinform.
Rui Liu, Zhengwu Zhang, Hyejung Won, J. S. Marron
2024 J jnl
CoRR
Stephen M. Pizer, Zhiyuan Liu, Junjie Zhao, Nicholas Tapp-Hughes, James N. Damon, Miaomiao Zhang, J. S. Marron, Jared Vicory
2024 J jnl
J. Comput. Graph. Stat.
Xi Yang, Jan Hannig, Katherine A. Hoadley, Iain Carmichael, J. S. Marron
2024 J jnl
CoRR
Yi Cui, Yao Li, Jayson R. Miedema, Sharon N. Edmiston, Sherif Farag, J. S. Marron, Nancy E. Thomas
2023 J jnl
J. Math. Imaging Vis.
Zhiyuan Liu, Jörn Schulz, Mohsen Taheri, Martin Styner, James N. Damon, Stephen M. Pizer, J. S. Marron
2023 J jnl
Int. J. Comput. Vis.
Zhiyuan Liu, James N. Damon, J. S. Marron, Stephen M. Pizer
2023 J jnl
Comput. Stat. Data Anal.
Xi Yang, Katherine A. Hoadley, Jan Hannig, J. S. Marron
2023 J jnl
J. Comput. Graph. Stat.
Pavlos Zoubouloglou, Eduardo García-Portugués, J. S. Marron
2023 J jnl
BMC Bioinform.
Yue Pan, T. Landis Justin, Razia Moorad, Di Wu, J. S. Marron, Dirk P. Dittmer
2023 J jnl
Comput. Stat. Data Anal.
Hyowon An, Kai Zhang, Hannu Oja, J. S. Marron
2023 conf
BIBM
Miray Unlu Yazici, Malik Yousef, J. S. Marron, Burcu Bakir-Gungor
2022 J jnl
CoRR
Yi Cui, Yao Li, Jayson R. Miedema, Sherif Farag, J. S. Marron, Nancy E. Thomas
2022 J jnl
Frontiers Comput. Sci.
Stephen M. Pizer, J. S. Marron, James N. Damon, Jared Vicory, Akash Krishna, Zhiyuan Liu, Mohsen Taheri
2021 B conf
Image Processing
Jared Vicory, Ramraj Chandradevan, Pablo Hernandez-Cerdan, Wei Angel Huang, Dani Fox, Laith Abu Qdais, Matthew McCormick, André Mol, Rick Walter, J. S. Marron, Hassem Geha, Asma Khan, Beatriz Paniagua
2021 J jnl
CoRR
Zhiyuan Liu, Jörn Schulz, Mohsen Taheri, Martin Styner, James N. Damon, Stephen M. Pizer, J. S. Marron
2021 J jnl
R J.
Andrew G. Allmon, J. S. Marron, Michael G. Hudgens
2020 J jnl
CoRR
Andrew G. Allmon, J. S. Marron, Michael G. Hudgens
2019 conf
Biomedical Applications in Molecular, Structural, and Functional Imaging
Jack Prothero, Jean-Baptiste Vimort, Antonio C. Ruellas, J. S. Marron, Matthew McCormick, Pablo Hernandez-Cerdan, Lucia H. S. Cevidanes, Erika Benavides, Beatriz Paniagua
2019 J jnl
CoRR
Heather D. Couture, Roland Kwitt, J. S. Marron, Melissa A. Troester, Charles M. Perou, Marc Niethammer
2019 conf
Biomedical Applications in Molecular, Structural, and Functional Imaging
Pablo Hernandez-Cerdan, Beatriz Paniagua, Jack Prothero, J. S. Marron, Eric Livingston, Ted Bateman, Matthew McCormick
2018 J jnl
J. Multivar. Anal.
Qing Feng, Meilei Jiang, Jan Hannig, J. S. Marron
2018 conf
Biomedical Applications in Molecular, Structural, and Functional Imaging
Jean-Baptiste Vimort, Antonio C. Ruellas, Jack Prothero, J. S. Marron, Matthew McCormick, Lucia H. S. Cevidanes, Erika Benavides, Beatriz Paniagua
2018 conf
MICCAI (2)
Heather D. Couture, J. S. Marron, Charles M. Perou, Melissa A. Troester, Marc Niethammer
2018 J jnl
CoRR
Heather D. Couture, J. S. Marron, Charles M. Perou, Melissa A. Troester, Marc Niethammer
2018 J jnl
SIAM J. Optim.
Sean Skwerer, J. Scott Provan, J. S. Marron
2017 J jnl
CoRR
Mónica Benito, Eduardo García-Portugués, J. S. Marron, Daniel Peña
2017 J jnl
CoRR
Iain Carmichael, J. S. Marron
2017 J jnl
NeuroImage
Qunqun Yu, Benjamin B. Risk, Kai Zhang, J. S. Marron
2016 J jnl
J. Mach. Learn. Res.
Dan Shen, Haipeng Shen, J. S. Marron
2016 J jnl
Stat. Anal. Data Min.
Patrick K. Kimes, David Neil Hayes, J. S. Marron, Yufeng Liu
2016 J jnl
Medical Image Anal.
Jun-Pyo Hong, Jared Vicory, Jörn Schulz, Martin Styner, J. S. Marron, Stephen M. Pizer
2016 J jnl
J. Math. Imaging Vis.
Jörn Schulz, Stephen M. Pizer, J. S. Marron, Fred Godtliebsen
2015 J jnl
Comput. Medical Imaging Graph.
Jared Vicory, Heather D. Couture, Nancy E. Thomas, David Borland, J. S. Marron, John T. Woosley, Marc Niethammer
2015 conf
ISBI
Heather D. Couture, J. S. Marron, Nancy E. Thomas, Charles M. Perou, Marc Niethammer
2015 J jnl
Comput. Stat. Data Anal.
Lingsong Zhang, Shu Lu, J. S. Marron
2014 J jnl
J. Multivar. Anal.
Petro Borysov, Jan Hannig, J. S. Marron
2014 J jnl
J. Math. Imaging Vis.
James N. Damon, J. S. Marron
2014 J jnl
CoRR
Patrick K. Kimes, D. Neil Hayes, J. S. Marron, Yufeng Liu
2014 J jnl
R J.
Jonathan Zhang, Nancy E. Heckman, Davor Cubranic, Joel G. Kingsolver, Travis Gaydos, J. S. Marron
2014 J jnl
Medical Image Anal.
Nikhil Singh, P. Thomas Fletcher, J. Samuel Preston, Richard D. King, J. S. Marron, Michael W. Weiner, Sarang C. Joshi
2014 J jnl
Medical Image Anal.
Yi Hong, Brad Davis, J. S. Marron, Roland Kwitt, Nikhil Singh, Julia S. Kimbell, Elizabeth Pitkin, Richard Superfine, Stephanie Davis, Carlton J. Zdanski, Marc Niethammer
2014 conf
MLMI
Nikhil Singh, Heather D. Couture, J. S. Marron, Charles M. Perou, Marc Niethammer
2014 J jnl
J. Math. Imaging Vis.
Sean Skwerer, Elizabeth Bullitt, Stephan Huckemann, Ezra Miller, Ipek Oguz, Megan Owen, Vic Patrangenaru, J. Scott Provan, J. S. Marron
2013 J jnl
J. Multivar. Anal.
Addy Bolívar-Cimé, J. S. Marron
2013 J jnl
J. Multivar. Anal.
Dan Shen, Haipeng Shen, J. S. Marron
2013 J jnl
CoRR
Lingsong Zhang, J. S. Marron, Shu Lu
2013 ch.
Innovations for Shape Analysis, Models and Algorithms
Stephen M. Pizer, Sungkyu Jung, Dibyendusekhar Goswami, Jared Vicory, Xiaojie Zhao, Ritwik Chaudhuri, James N. Damon, Stephan Huckemann, J. S. Marron
2013 conf
MICCAI (3)
Yi Hong, Brad Davis, J. S. Marron, Roland Kwitt, Marc Niethammer
2012 J jnl
J. Multivar. Anal.
Sungkyu Jung, Arusharka Sen, J. S. Marron
2012 J jnl
Bioinform.
Hanwen Huang, Xiaosun Lu, Yufeng Liu, Perry Haaland, J. S. Marron
2012 J jnl
BMC Bioinform.
Christopher R. Cabanski, Keary Cavin, Chris Bizon, Matthew D. Wilkerson, Joel S. Parker, Kirk C. Wilhelmsen, Charles M. Perou, J. S. Marron, D. Neil Hayes
2010 J jnl
CoRR
Cheolwoo Park, Félix Hernández-Campos, J. S. Marron, Kevin Jeffay, F. Donelson Smith
2010 conf
MLMI
Marc Niethammer, David Borland, J. S. Marron, John T. Woosley, Nancy E. Thomas
2010 J jnl
BMC Bioinform.
Eric F. Lock, Ryan Ziemiecki, J. S. Marron, Dirk P. Dittmer
2010 J jnl
IEEE Trans. Pattern Anal. Mach. Intell.
Kevin Gorczowski, Martin Styner, Ja-Yeon Jeong, J. S. Marron, Joseph Piven, Heather Cody Hazlett, Stephen M. Pizer, Guido Gerig
2010 conf
Multimedia Information Retrieval
J. S. Marron, Sungkyu Jung, Ian L. Dryden
2009 conf
ISBI
Marc Macenko, Marc Niethammer, J. S. Marron, David Borland, John T. Woosley, Xiaojun Guan, Charles Schmitt, Nancy E. Thomas
2007 B conf
Image Processing
Kevin Gorczowski, Martin Styner, Ja-Yeon Jeong, J. S. Marron, Joseph Piven, Heather Cody Hazlett, Stephen M. Pizer, Guido Gerig
2007 A* conf
CVPR
Kevin Gorczowski, Martin Styner, Ja-Yeon Jeong, J. S. Marron, Joseph Piven, Heather Cody Hazlett, Stephen M. Pizer, Guido Gerig
2007 J jnl
Comput. Stat. Data Anal.
Cheolwoo Park, Fred Godtliebsen, Murad S. Taqqu, Stilian Stoev, J. S. Marron
2006 B conf
MASCOTS
Cheolwoo Park, H. Shen, J. S. Marron, Félix Hernández-Campos, Darryl Veitch
2006 J jnl
Comput. Stat. Data Anal.
Stilian Stoev, Murad S. Taqqu, Cheolwoo Park, George Michailidis, J. S. Marron
2005 J jnl
Comput. Networks
Cheolwoo Park, Félix Hernández-Campos, J. S. Marron, F. Donelson Smith
2005 J jnl
Comput. Networks
Stilian Stoev, Murad S. Taqqu, Cheolwoo Park, J. S. Marron
2005 J jnl
Comput. Stat.
J. S. Marron, Jin Ting Zhang
2004 J jnl
Perform. Evaluation
Félix Hernández-Campos, J. S. Marron, Gennady Samorodnitsky, F. Donelson Smith
2002 conf
COMPSTAT
J. S. Marron, Félix Hernández-Campos, F. Donelson Smith
2002 J jnl
IEEE Trans. Medical Imaging
Sarang C. Joshi, Stephen M. Pizer, P. Thomas Fletcher, Paul A. Yushkevich, Andrew Thall, J. S. Marron
2002 B conf
MASCOTS
Félix Hernández-Campos, J. S. Marron, F. Donelson Smith, Gennady Samorodnitsky
2001 J jnl
IEEE Trans. Software Eng.
Stephen G. Eick, Todd L. Graves, Alan F. Karr, J. S. Marron, Audris Mockus
2001 conf
IPMI
Paul A. Yushkevich, Stephen M. Pizer, Sarang C. Joshi, J. S. Marron
2001 J jnl
Comput. Stat.
J. S. Marron, S. S. Chung
2000 J jnl
IEEE Trans. Software Eng.
Todd L. Graves, Alan F. Karr, J. S. Marron, Harvey P. Siy
1999 J jnl
Stat. Comput.
J. S. Marron, F. Udina
redb/extractors/js_extractors/js_context.py
← Index redb/extractors/js_extractors/js_context.py python
"""Per-sample shared state for the JavaScript extractor pipeline.

A `JSContext` is built exactly once per JS sample (in `workers.py`) and threaded
into every extractor that runs against that sample. It owns the disk read, the
decoded source text, the line-split cache, the Shannon text-entropy figure, the
shared `scan_source()` results, and the pyjsparser AST. Each of those is
computed lazily through `cached_property` so an extractor that doesn't need a
particular artefact does not pay for it.

Without this object, every JS extractor instance redoes the same disk read,
decode, scan, and (for any consumer) AST parse. With it, every extractor
shares one set of results.

`JSExtractor.__init__` accepts the context via a `context=` kwarg; if absent
(e.g. unit tests instantiating an extractor directly with `source=...`) it
builds a fresh context from the constructor arguments. Either path produces a
fully-populated context, so extractor code can always rely on
`self._context.scan` / `self._context.ast` / etc.
"""

from __future__ import annotations

import math
from collections import Counter
from dataclasses import dataclass
from functools import cached_property
from typing import Any, Dict, List, Optional

import chardet

from redb.extractors.js_extractors.js_patterns import scan_source


def decode_source(raw_bytes: bytes) -> str:
    """Decode raw JS bytes to text, honouring BOMs and falling back to chardet.

    Mirrors the historical `JSExtractor._decode_source` logic so existing tests
    continue to round-trip identically.
    """
    if not raw_bytes:
        return ""

    if raw_bytes[:3] == b"\xef\xbb\xbf":
        return raw_bytes[3:].decode("utf-8", errors="replace")
    if raw_bytes[:2] in (b"\xff\xfe", b"\xfe\xff"):
        return raw_bytes.decode("utf-16", errors="replace")

    try:
        return raw_bytes.decode("utf-8")
    except UnicodeDecodeError:
        pass

    try:
        detected = chardet.detect(raw_bytes)
        if detected and detected.get("encoding"):
            return raw_bytes.decode(detected["encoding"], errors="replace")
    except Exception:
        pass

    return raw_bytes.decode("latin-1", errors="replace")


def _text_entropy(text: str) -> float:
    """Shannon entropy of the character distribution of `text`, rounded to 4dp."""
    if not text:
        return 0.0
    counter = Counter(text)
    length = len(text)
    entropy = 0.0
    for count in counter.values():
        p = count / length
        if p > 0:
            entropy -= p * math.log2(p)
    return round(entropy, 4)


@dataclass
class JSContext:
    """Shared raw materials for one JS sample, consumed by every JS extractor.

    Cheap attributes (raw_bytes, source) are populated eagerly by the factory.
    Expensive ones (scan, ast) are cached_property — computed on first access
    and reused across every extractor that holds the same context.

    `content_type` is the magika label (e.g. `"javascript"`) carried alongside
    the source so the new code_text_content writer (and any future generic
    text-content writer) can record it without re-running magika. Defaults to
    `"javascript"` because by construction this context type is JS-specific;
    workers.py supplies the actual magika value when it builds the context.
    """

    filepath: str
    raw_bytes: bytes
    source: str
    log: Any = None
    content_type: str = "javascript"
    # Populated by JSStringsExtractor.extract() (the decoded/reconstructed
    # strings — hex/unicode/charcode/base64/concat unpacked into plaintext).
    # Read post-loop by the IOC plumbing in workers.py so any IOCs hidden
    # behind those encodings get scraped from the decoded form. Stays None
    # if JSStringsExtractor didn't run for this sample.
    decoded_strings: Optional[list] = None

    @cached_property
    def lines(self) -> List[str]:
        return self.source.splitlines() if self.source else []

    @cached_property
    def text_entropy(self) -> float:
        return _text_entropy(self.source)

    @cached_property
    def scan(self) -> Dict[str, Dict[str, object]]:
        """Result of running scan_source() exactly once over self.source."""
        return scan_source(self.source) if self.source else {}

    @cached_property
    def ast(self) -> Optional[Any]:
        """Lazy pyjsparser AST. Returns None if the parser is missing or fails.

        Extractors should treat None AST as "fall back to regex" — every
        AST-consuming extractor already handles that path.
        """
        if not self.source:
            return None
        try:
            import pyjsparser
            return pyjsparser.parse(self.source)
        except ImportError:
            if self.log is not None:
                self.log.debug("pyjsparser not installed, AST analysis skipped")
        except Exception as e:
            if self.log is not None:
                self.log.warning(f"AST parsing failed for {self.filepath}: {e}")
        return None

    @cached_property
    def deobfuscated(self) -> "tuple[Optional[str], Optional[str]]":
        """Run the configured JS deobfuscator (with jsbeautifier fallback) once
        per sample and cache the result. Returns `(text, normalizer_used)` or
        `(None, None)` if neither path produced output.

        Computed lazily on first access — samples whose pipeline never reads
        this don't pay the subprocess cost.
        """
        from redb.extractors.js_extractors.js_deobfuscator import deobfuscate
        return deobfuscate(self.source, self.log)

    @cached_property
    def scan_deobfuscated(self) -> Dict[str, Dict[str, object]]:
        """Result of running scan_source() exactly once over the deobfuscated
        text, keyed by PATTERNS only (FEATURE_PATTERNS are not consulted by
        the dual-pass consumers). Empty dict when there is no deobfuscated
        text or it equals the raw source.

        Two extractors consume the post-deobf API surface:
        `JSSuspiciousAPIsExtractor` (for revealed_by_deobf rows) and
        `JSDeobfuscationExtractor` (for the new_apis_found diff). Caching here
        means we scan the deobfuscated text once instead of twice per sample.
        """
        from redb.extractors.js_extractors.js_patterns import PATTERNS
        deobf_text, _ = self.deobfuscated
        if not deobf_text or deobf_text == self.source:
            return {}
        return scan_source(deobf_text, patterns=(PATTERNS,))

    @cached_property
    def xray(self):
        """Run @nodesecure/js-x-ray once per sample and cache the result.

        Returns an `XRayResult` (always — the function collapses every failure
        path to an empty result so callers don't have to special-case missing
        Node, missing package, timeouts, or parse errors). The
        `JSFeaturesExtractor` reads it for the obfuscator family name and for
        corroborating warning kinds; the heuristic falls back cleanly when
        `xray.obfuscator is None`.
        """
        from redb.extractors.js_extractors.js_xray import run
        return run(self.source, self.log)

    @classmethod
    def from_path(
        cls,
        filepath: str,
        log: Any = None,
        source: Optional[str] = None,
        raw_bytes: Optional[bytes] = None,
        content_type: str = "javascript",
    ) -> "JSContext":
        """Build a context from disk. `raw_bytes` and `source` are optional
        overrides — useful when the caller has already read or decoded the file.
        `content_type` is the magika label workers.py dispatched on; it lands
        on the context for the code_text_content writer to record.
        """
        if raw_bytes is None:
            with open(filepath, "rb") as f:
                raw_bytes = f.read()
        if source is None:
            source = decode_source(raw_bytes)
        return cls(
            filepath=filepath,
            raw_bytes=raw_bytes,
            source=source,
            log=log,
            content_type=content_type,
        )