Manav Mahan Singh

11 papers Journal 9Unranked 2
YearRankTypeTitle / Venue / Authors
2026 J jnl
Enrichment of building energy models using error domain constrained generative machine learning.
Adv. Eng. Informatics
Manav Mahan Singh, Klara Santer, José Quesada-Allerhand, Ian F. C. Smith
2024 J jnl
Explainable AI for engineering design: A unified approach of systems engineering and component-based deep learning demonstrated by energy-efficient building design.
Adv. Eng. Informatics
Philipp Geyer, Manav Mahan Singh, Xia Chen
2024 J jnl
Utilizing domain knowledge: Robust machine learning for building energy performance prediction with small, inconsistent datasets.
Knowl. Based Syst.
Xia Chen, Manav Mahan Singh, Philipp Geyer
2023 J jnl
Utilizing Domain Knowledge: Robust Machine Learning for Building Energy Prediction with Small, Inconsistent Datasets.
CoRR
Xia Chen, Manav Mahan Singh, Philipp Geyer
2022 J jnl
Introducing causal inference in the energy-efficient building design process.
CoRR
Xia Chen, Jimmy Abualdenien, Manav Mahan Singh, André Borrmann, Philipp Geyer
2021 J jnl
Explainable AI for engineering design: A unified approach of systems engineering and component-based deep learning.
CoRR
Philipp Geyer, Manav Mahan Singh, Xia Chen
2020 J jnl
Consistent management and evaluation of building models in the early design stages.
J. Inf. Technol. Constr.
Jimmy Abualdenien, Patricia Schneider-Marin, Ata Zahedi, Hannes Harter, Hannah Exner, Daniel Steiner, Manav Mahan Singh, André Borrmann, Werner Lang, Frank Petzold, Markus König, Philipp Geyer, Martina Schnellenbach-Held
2020 J jnl
Information requirements for multi-level-of-development BIM using sensitivity analysis for energy performance.
Adv. Eng. Informatics
Manav Mahan Singh, Philipp Geyer
2020 J jnl
Quick energy prediction and comparison of options at the early design stage.
Adv. Eng. Informatics
Manav Mahan Singh, Sundaravelpandian Singaravel, Ralf Klein, Philipp Geyer
2018 — conf
Component-Based Machine Learning for Energy Performance Prediction by MultiLOD Models in the Early Phases of Building Design.
EG-ICE
Philipp Geyer, Manav Mahan Singh, Sundaravelpandian Singaravel
2018 — conf
Statistical Decision Assistance for Determining Energy-Efficient Options In Building Design Under Uncertainty.
EG-ICE
Manav Mahan Singh, Philipp Geyer
redb/extractors/enum.py
← Index redb/extractors/enum.py python 
from enum import Enum


class Tag(Enum):
    BASIC_PROPERTIES = "basic_properties"
    DIEC = "detect_it_easy"
    CAPA = "capa"
    MALCONTENT = "malcontent"
    DECOMPILED = "decompiled"
    HASH = "hash"
    STRINGS = "strings"
    RELATED = "relatedness" # unused
    PE_FEATURES = "pe_features"
    PE_IMPORT = "pe_import"
    PE_OVERLAY = "pe_overlay"
    PE_SECTION = "pe_section"
    PE_RESOURCE = "pe_resource"
    PE_SIGNATURE = "pe_signature"
    PE_CERTIFICATE = "pe_certificate"
    PE_SIGNER = "pe_signer"
    PE_DOTNET = "pe_dotnet"
    PE_EMBEDDED_EXTRAS = "pe_embedded_extras"
    PE_INCONSISTENCY_TESTS = "pe_inconsistency_tests"
    DOTNET_INCONSISTENCY_TESTS = "dotnet_inconsistency_tests"
    YARA = "yara"
    # Mach-O
    MACHO_FEATURES = "macho_features"
    MACHO_IMPORT = "macho_import"
    MACHO_EXPORT = "macho_export"
    MACHO_DYLIB = "macho_dylib"
    MACHO_SEGMENT = "macho_segment"
    MACHO_SIGNATURE = "macho_signature"
    MACHO_CERTIFICATE = "macho_certificate"
    MACHO_ENTITLEMENT = "macho_entitlement"
    MACHO_UNIVERSAL = "macho_universal"
    MACHO_HASHES = "macho_hashes"
    # ELF
    ELF_FEATURES = "elf_features"
    ELF_IMPORT = "elf_import"
    ELF_EXPORT = "elf_export"
    ELF_SECTION = "elf_section"
    ELF_SEGMENT = "elf_segment"
    ELF_SYMBOL = "elf_symbol"
    ELF_RELOCATION = "elf_relocation"
    ELF_NOTE = "elf_note"
    ELF_DEPENDENCY = "elf_dependency"
    # APK
    APK_FEATURES = "apk_features"
    APK_MANIFEST = "apk_manifest"
    APK_PERMISSIONS = "apk_permissions"
    APK_SIGNATURE = "apk_signature"
    APK_DEX = "apk_dex"
    APK_RESOURCES = "apk_resources"
    APK_NATIVE_LIBS = "apk_native_libs"
    APK_INCONSISTENCY_TESTS = "apk_inconsistency_tests"
    APK_DECOMPILED = "apk_decompiled"
    # JavaScript
    JS_FEATURES = "js_features"
    JS_SUSPICIOUS_APIS = "js_suspicious_apis"
    JS_STRINGS = "js_strings"
    JS_DEOBFUSCATION = "js_deobfuscation"
    JS_CONTENT = "js_content"