James J. Gross

26 papers A* 6B 1Journal 16Unranked 3
YearRankTypeTitle / Venue / Authors
2025 J jnl
npj Digit. Medicine
Jianjie Xu, Zhuo Rachel Han, Xiangxi Lv, Lu Chen, Ningning Mao, Mengyu Miranda Gao, Hanyi Zhang, Yueqin Hu, Shaozheng Qin, James J. Gross, Zhanjun Zhang
2025 A* conf
CHI
Seray B. Ibrahim, Predrag Klasnja, James J. Gross, Petr Slovák
2025 B conf
CogSci
Kate Petrova, James J. Gross, Tobias Gerstenberg
2025 A* conf
CHI
Amira Skeggs, Ashish Mehta, Valerie Yap, Seray B. Ibrahim, Charla Rhodes, James J. Gross, Sean A. Munson, Predrag Klasnja, Amy C. Orben, Petr Slovák
2024 J jnl
CoRR
Lucia Chen, David A. Preece, Pilleriin Sikka, James J. Gross, Ben Krause
2023 J jnl
IEEE Trans. Affect. Comput.
Pardis Miri, Horia Margarit, Andero Uusberg, Keith Marzullo, Tali M. Ball, Daniel Yamins, Robert Flory, James J. Gross
2023 J jnl
ACM Trans. Comput. Hum. Interact.
Petr Slovák, Alissa Nicole Antle, Nikki Theofanopoulou, Claudia Daudén Roquet, James J. Gross, Katherine Isbister
2023 J jnl
NeuroImage
Ryan J. Murray, Sylvia D. Kreibig, Corinna Pehrs, Patrik Vuilleumier, James J. Gross, Andrea C. Samson
2022 J jnl
CoRR
Petr Slovák, Alissa Nicole Antle, Nikki Theofanopoulou, Claudia Daudén Roquet, James J. Gross, Katherine Isbister
2022 A* conf
CHI
Wally Smith, Greg Wadley, Sarah Webber, Benjamin Tag, Vassilis Kostakos, Peter Koval, James J. Gross
2022 A* conf
CHI
Claudia Daudén Roquet, Nikki Theofanopoulou, Jaimie L. Freeman, Jessica Schleider, James J. Gross, Katie Davis, Ellen Townsend, Petr Slovák
2022 A* conf
CHI
Pardis Miri, Mehul Arora, Aman Malhotra, Robert Flory, Stephanie Hu, Ashley Lowber, Ishan Goyal, Jacqueline Nguyen, John P. Hegarty, Marlo D. Kohn, David Schneider, Heather Culbertson, Daniel L. K. Yamins, Lawrence Fung, Antonio Hardan, James J. Gross, Keith Marzullo
2022 J jnl
Frontiers Digit. Health
Benjamin T. Kaveladze, Robert R. Morris, Rosa Victoria Dimitrova-Gammeltoft, Amit Goldenberg, James J. Gross, Judd Antin, Melissa Sandgren, Melissa C. Thomas-Hunt
2022 conf
CHI Extended Abstracts
Greg Wadley, Vassilis Kostakos, Peter Koval, Wally Smith, Sarah Webber, Anna L. Cox, James J. Gross, Kristina Höök, Regan L. Mandryk, Petr Slovák
2020 A* conf
CHI
Pardis Miri, Emily Jusuf, Andero Uusberg, Horia Margarit, Robert Flory, Katherine Isbister, Keith Marzullo, James J. Gross
2020 J jnl
NeuroImage
Joshua Gertler, Stephanie Novotny, Andrew Poppe, Yu Sun Chung, James J. Gross, Godfrey D. Pearlson, Michael C. Stevens
2020 J jnl
ACM Trans. Comput. Hum. Interact.
Pardis Miri, Robert Flory, Andero Uusberg, Heather Culbertson, Richard H. Harvey, Agata Kelman, Davis Erik Peper, James J. Gross, Katherine Isbister, Keith Marzullo
2018 conf
CHI Extended Abstracts
Pardis Miri, Andero Uusberg, Heather Culbertson, Robert Flory, Helen Uusberg, James J. Gross, Keith Marzullo, Katherine Isbister
2017 conf
CHI Extended Abstracts
Pardis Miri, Robert Flory, Andero Uusberg, Helen Uusberg, James J. Gross, Katherine Isbister
2011 J jnl
NeuroImage
Nicole R. Giuliani, Emily M. Drabant, Roshni Bhatnagar, James J. Gross
2011 J jnl
NeuroImage
Emily M. Drabant, Janice R. Kuo, Wiveka Ramel, Jens Blechert, Michael D. Edge, Jeff R. Cooper, Philippe R. Goldin, Ahmad R. Hariri, James J. Gross
2010 J jnl
J. Cogn. Neurosci.
Kateri McRae, Brent L. Hughes, Sita Chopra, John D. E. Gabrieli, James J. Gross, Kevin N. Ochsner
2008 J jnl
Int. J. Hum. Comput. Stud.
Jeremy N. Bailenson, Emmanuel D. Pontikakis, Iris B. Mauss, James J. Gross, Maria E. Jabon, Cendri A. C. Hutcherson, Clifford Nass, Oliver John
2005 J jnl
NeuroImage
Cendri A. C. Hutcherson, Philippe R. Goldin, Kevin N. Ochsner, John D. E. Gabrieli, Lisa Feldman Barrett, James J. Gross
2005 J jnl
NeuroImage
Philippe R. Goldin, Cendri A. C. Hutcherson, Kevin N. Ochsner, Gary H. Glover, John D. E. Gabrieli, James J. Gross
2004 J jnl
NeuroImage
Kevin N. Ochsner, Rebecca D. Ray, Jeffrey C. Cooper, Elaine R. Robertson, Sita Chopra, John D. E. Gabrieli, James J. Gross
redb/extractors/macho_extractors/macho_dylibs.py
← Index redb/extractors/macho_extractors/macho_dylibs.py python
import hashlib
import inspect
from datetime import datetime, timezone
from typing import Any

from redb.extractors.enum import Tag
from redb.extractors.macho_extractor import MachOExtractor
from redb.models.dataclasses import MachODylib


class MachODylibExtractor(MachOExtractor):

    def __init__(
        self,
        filepath,
        log,
        exporters=None,
        index_prefix=None,
        elastic_index=None,
        known_benign=False,
        known_malicious=False,
        macho=None,
    ):
        super().__init__(
            filepath,
            log,
            exporters,
            index_prefix,
            elastic_index,
            known_benign,
            known_malicious,
            macho,
        )
        self.elastic_index = self.index_prefix + "-macho_dylibs"
        self.log.debug(inspect.currentframe().f_code.co_name)

    def tag(self):
        return Tag.MACHO_DYLIB.value

    def _extract_dylibs(self):
        """Extract dynamic library information from all architectures in the MachO binary."""
        self.log.debug(inspect.currentframe().f_code.co_name)
        dylibs = []

        if not self.macho:
            return dylibs

        try:
            # Get architectures using new API (already parsed in base class)
            architectures = self.macho.get_architectures()
            if not architectures:
                return dylibs

            # Process each architecture
            for arch_name in architectures:
                # Get dylib commands using new API with architecture parameter
                dylib_commands = self.macho.get_dylib_commands(arch=arch_name)
                if not dylib_commands:
                    continue

                # Extract dylib commands for this architecture
                for dylib_cmd in dylib_commands:
                    try:
                        dylib_name = dylib_cmd.get('dylib_name', 'Unknown')
                        if isinstance(dylib_name, bytes):
                            dylib_name = dylib_name.decode('utf-8', errors='replace')

                        # Create dylib dataclass with architecture info
                        macho_dylib = MachODylib(
                            dylib_name=dylib_name,
                            dylib_timestamp=dylib_cmd.get('dylib_timestamp', 0),
                            dylib_current_version=dylib_cmd.get('dylib_current_version', 0),
                            dylib_compat_version=dylib_cmd.get('dylib_compat_version', 0),
                        )
                        # Add architecture info to the dylib
                        macho_dylib.architecture = arch_name
                        dylibs.append(macho_dylib)

                    except Exception as e:
                        self.log.warning(
                            f'Unable to process dylib "{dylib_cmd.get("dylib_name", "Unknown")}" for architecture {arch_name} in {self.hash.sha256}: {e}'
                        )
                        continue

            return dylibs

        except Exception as e:
            self.log.error(f"Error extracting MachO dylibs: {e}")
            return dylibs

    def extract(self):
        self.log.debug(inspect.currentframe().f_code.co_name)
        try:
            dylibs = self._extract_dylibs()
            return dylibs
        except Exception as e:
            self.log.error(f"Error extracting MachO dylibs: {e}")
            return None

    def prepare_export_data(self, exporter_type: str) -> Any:
        if exporter_type == "ElasticsearchExporter":
            return self.extract()
        elif exporter_type == "ClickHouseExporter":
            if not self.macho:
                return None

            # Get architectures (macho is already parsed in base class)
            try:
                architectures = self.macho.get_architectures()
                is_fat = len(architectures) > 1
            except Exception as e:
                self.log.error(f"Could not get architectures: {e}")
                return None

            data = []
            current_time = datetime.now(timezone.utc)

            # Loop through each architecture (1 for single, multiple for FAT)
            for arch_name in architectures:
                # Get architecture-specific sha256
                try:
                    arch_general_info = self.macho.get_general_info(arch=arch_name)
                    arch_header_raw = self.macho.get_macho_header(arch=arch_name)
                    arch_sha256 = arch_general_info.get('SHA256', self.sha256)
                    arch_cputype_raw = arch_header_raw.get('cputype', 0) if arch_header_raw else 0
                except Exception as e:
                    self.log.warning(f"Could not get arch-specific data for {arch_name}: {e}")
                    arch_sha256 = self.sha256
                    arch_cputype_raw = 0

                # Get dylib commands for this architecture
                dylib_commands = self.macho.get_dylib_commands(arch=arch_name)
                if not dylib_commands:
                    continue

                # Process each dylib for this architecture
                for dylib_cmd in dylib_commands:
                    try:
                        dylib_name = dylib_cmd.get('dylib_name', 'Unknown')
                        if isinstance(dylib_name, bytes):
                            dylib_name = dylib_name.decode('utf-8', errors='replace')

                        data.append([
                            arch_sha256,                          # sha256 (architecture-specific)
                            dylib_name,                           # dylib_name
                            dylib_cmd.get('dylib_timestamp', 0), # dylib_timestamp
                            dylib_cmd.get('dylib_current_version', 0), # dylib_current_version
                            dylib_cmd.get('dylib_compat_version', 0),  # dylib_compat_version
                            current_time,                         # analysis_date
                        ])
                    except Exception as e:
                        self.log.warning(
                            f'Unable to process dylib "{dylib_cmd.get("dylib_name", "Unknown")}" for architecture {arch_name}: {e}'
                        )
                        continue

            column_names = [
                'sha256',
                'dylib_name', 'dylib_timestamp', 'dylib_current_version',
                'dylib_compat_version', 'analysis_date'
            ]

            if not data:
                return None

            column_type_names = [
                'FixedString(64)',
                'String', 'UInt32', 'UInt32',
                'UInt32', 'DateTime64(3, \'UTC\')'
            ]

            return (data, column_names, column_type_names)

        return None

    def get_clickhouse_table(self) -> str:
        return "redb_macho_dylibs"