Torram

torram

[Toronto, Canada] Nov 14, 2024 – Torram, pioneering institutional-grade decentralized finance (DeFi) infrastructure on Bitcoin, today announced a strategic investment from Draper Associates, the venture firm led by legendary crypto investor Tim Draper. Selected from over 200 applicants for the highly exclusive BitcoinFi Accelerator, Torram is now better positioned to capture the rapidly expanding institutional DeFi market as Bitcoin emerges as the leading blockchain for traditional finance. The investment, led by Draper Associates with participation from Boost VC and Thesis, comes as institutional demand for Bitcoin-native DeFi solutions surges, with the DeFi market set to soar to $50 billion by 2025. Torram's institutional-grade infrastructure enables banks, asset managers, and financial institutions to tap into Bitcoin's security and $880 billion market cap through compliant DeFi solutions. "We believe in Bitcoin and there’s now a gravitational pull towards Bitcoin," said Tim Draper, Founder of Draper Associates. "It’s a critical time in global history, and we’re excited about these applications that are being built on the most important blockchain.” Torram emerged as one of the top performers from Bitcoin Startup Labs' pre-accelerator program and has now earned one of only eight spots in BitcoinFi Accelerator's inaugural cohort. The exclusive 6-week program, backed by leading Bitcoin investors, provides $150,000 in initial funding alongside technical resources and strategic partnerships essential for scaling Bitcoin-native applications. "Securing backing from visionaries like Tim Draper and Adam Draper validates our approach to bridging traditional finance (TradFi) with Bitcoin's powerful capabilities," said Vakeesan Mahalingam, CEO of Torram. "Their strategic guidance and deep network in both TradFi and crypto will be instrumental as we roll out our institutional DeFi infrastructure." Torram's technology stack includes: An institutional-grade decentralized oracle network
Advanced on-chain data indexing for real-time analytics
Proprietary security framework meeting institutional compliance requirements
Cross-chain infrastructure enabling seamless integration with existing systems The company will launch its testnet in 2025. Early access to the testnet is in high demand and is limited to qualified institutions and Bitcoin ecosystem partners. For early investment opportunities or to learn more about how Torram is positioning itself to lead the Bitcoin DeFi revolution, visit torram.xyz. About Torram Torram is building the foundational infrastructure that enables institutional-grade DeFi and real-world asset tokenization natively on Bitcoin. Torram empowers financial institutions to leverage Bitcoin's unmatched security, transparency, and $880 billion of global liquidity through its decentralized oracle network, on-chain data indexing, and institutional-grade solutions. This press release contains forward-looking statements, including statements regarding Torram’s plans, objectives, and future business operations. These forward-looking statements are based on the company’s current expectations, beliefs, and projections, and are subject to inherent risks and uncertainties. Actual results may differ materially from those expressed or implied by these statements due to various factors, including market conditions, regulatory changes, technological challenges, and competition.
The information in this release is not intended to constitute legal, financial, or investment advice. Readers should conduct their own research and consult with professional advisors before making any investment decisions or entering into any agreements. Torram does not guarantee any specific outcomes or returns on investment.Torram does not undertake any obligation to update or revise any forward-looking statements, whether as a result of new information, future events, or otherwise, except as required by applicable law. All trademarks and registered trademarks are the property of their respective owners. [Toronto, Canada] Nov 14, 2024 – Torram, pioneering institutional-grade decentralized finance (DeFi) infrastructure on Bitcoin, today announced a strategic investment from Draper Associates, the venture firm led by legendary crypto investor Tim Draper. Selected from over 200 applicants for the highly exclusive BitcoinFi Accelerator, Torram is now better positioned to capture the rapidly expanding institutional DeFi market as Bitcoin emerges as the leading blockchain for traditional finance. [Toronto, Canada] Nov 14, 2024 – Torram, pioneering institutional-grade decentralized finance (DeFi) infrastructure on Bitcoin, today announced a strategic investment from Draper Associates, the venture firm led by legendary crypto investor Tim Draper. Selected from over 200 applicants for the highly exclusive BitcoinFi Accelerator, Torram is now better positioned to capture the rapidly expanding institutional DeFi market as Bitcoin emerges as the leading blockchain for traditional finance. The investment, led by Draper Associates with participation from Boost VC and Thesis, comes as institutional demand for Bitcoin-native DeFi solutions surges, with the DeFi market set to soar to $50 billion by 2025. Torram's institutional-grade infrastructure enables banks, asset managers, and financial institutions to tap into Bitcoin's security and $880 billion market cap through compliant DeFi solutions. "We believe in Bitcoin and there’s now a gravitational pull towards Bitcoin," said Tim Draper, Founder of Draper Associates. "It’s a critical time in global history, and we’re excited about these applications that are being built on the most important blockchain.” "We believe in Bitcoin and there’s now a gravitational pull towards Bitcoin," said Tim Draper, Founder of Draper Associates. "It’s a critical time in global history, and we’re excited about these applications that are being built on the most important blockchain.” Torram emerged as one of the top performers from Bitcoin Startup Labs' pre-accelerator program and has now earned one of only eight spots in BitcoinFi Accelerator's inaugural cohort. The exclusive 6-week program, backed by leading Bitcoin investors, provides $150,000 in initial funding alongside technical resources and strategic partnerships essential for scaling Bitcoin-native applications. "Securing backing from visionaries like Tim Draper and Adam Draper validates our approach to bridging traditional finance (TradFi) with Bitcoin's powerful capabilities," said Vakeesan Mahalingam, CEO of Torram. "Their strategic guidance and deep network in both TradFi and crypto will be instrumental as we roll out our institutional DeFi infrastructure." "Securing backing from visionaries like Tim Draper and Adam Draper validates our approach to bridging traditional finance (TradFi) with Bitcoin's powerful capabilities," said Vakeesan Mahalingam, CEO of Torram. "Their strategic guidance and deep network in both TradFi and crypto will be instrumental as we roll out our institutional DeFi infrastructure." Torram's technology stack includes: An institutional-grade decentralized oracle network Advanced on-chain data indexing for real-time analytics Proprietary security framework meeting institutional compliance requirements Cross-chain infrastructure enabling seamless integration with existing systems An institutional-grade decentralized oracle network Advanced on-chain data indexing for real-time analytics Proprietary security framework meeting institutional compliance requirements Cross-chain infrastructure enabling seamless integration with existing systems The company will launch its testnet in 2025. Early access to the testnet is in high demand and is limited to qualified institutions and Bitcoin ecosystem partners. For early investment opportunities or to learn more about how Torram is positioning itself to lead the Bitcoin DeFi revolution, visit torram.xyz . torram.xyz About Torram Torram is building the foundational infrastructure that enables institutional-grade DeFi and real-world asset tokenization natively on Bitcoin. Torram empowers financial institutions to leverage Bitcoin's unmatched security, transparency, and $880 billion of global liquidity through its decentralized oracle network, on-chain data indexing, and institutional-grade solutions. This press release contains forward-looking statements, including statements regarding Torram’s plans, objectives, and future business operations. These forward-looking statements are based on the company’s current expectations, beliefs, and projections, and are subject to inherent risks and uncertainties. Actual results may differ materially from those expressed or implied by these statements due to various factors, including market conditions, regulatory changes, technological challenges, and competition. The information in this release is not intended to constitute legal, financial, or investment advice. Readers should conduct their own research and consult with professional advisors before making any investment decisions or entering into any agreements. Torram does not guarantee any specific outcomes or returns on investment. Torram does not undertake any obligation to update or revise any forward-looking statements, whether as a result of new information, future events, or otherwise, except as required by applicable law. All trademarks and registered trademarks are the property of their respective owners. This press release contains forward-looking statements, including statements regarding Torram’s plans, objectives, and future business operations. These forward-looking statements are based on the company’s current expectations, beliefs, and projections, and are subject to inherent risks and uncertainties. Actual results may differ materially from those expressed or implied by these statements due to various factors, including market conditions, regulatory changes, technological challenges, and competition. This press release contains forward-looking statements, including statements regarding Torram’s plans, objectives, and future business operations. These forward-looking statements are based on the company’s current expectations, beliefs, and projections, and are subject to inherent risks and uncertainties. Actual results may differ materially from those expressed or implied by these statements due to various factors, including market conditions, regulatory changes, technological challenges, and competition. The information in this release is not intended to constitute legal, financial, or investment advice. Readers should conduct their own research and consult with professional advisors before making any investment decisions or entering into any agreements. Torram does not guarantee any specific outcomes or returns on investment. The information in this release is not intended to constitute legal, financial, or investment advice. Readers should conduct their own research and consult with professional advisors before making any investment decisions or entering into any agreements. Torram does not guarantee any specific outcomes or returns on investment. Torram does not undertake any obligation to update or revise any forward-looking statements, whether as a result of new information, future events, or otherwise, except as required by applicable law. All trademarks and registered trademarks are the property of their respective owners. Torram does not undertake any obligation to update or revise any forward-looking statements, whether as a result of new information, future events, or otherwise, except as required by applicable law. All trademarks and registered trademarks are the property of their respective owners.

Draper Associates Backs Torram to Propel Decentralized Finance on Bitcoin

Draper Associates recolza Torram per impulsar el finançament descentralitzat a Bitcoin

Draper Associates respalda a Torram para impulsar las finanzas descentralizadas en Bitcoin

ड्रेपर एसोसिएट्स ने बिटकॉइन पर विकेंद्रीकृत वित्त को बढ़ावा देने के लिए टोरम का समर्थन किया

Draper Associates-ը աջակցում է Torram-ին ապակենտրոնացված ֆինանսները բիթքոյնով առաջ մղելու համար

Draper Associates Mendukung Torram untuk Mendorong Keuangan Terdesentralisasi pada Bitcoin

ドレイパー・アソシエイツ、ビットコインによる分散型金融を推進するトーラムを支援

Draper Associates Торрамды Биткойн боюнча борборлоштурулган каржыны жайылтуу үчүн колдойт

Draper Associates esungaki Torram mpo na ko propulser Finances décentralisées na Bitcoin

Ny Draper Associates dia manohana an'i Torram hanohanana ny fitantanam-bola tsy miankina amin'ny Bitcoin

Draper Associates stöder Torram för att driva decentraliserad finans på Bitcoin

Draper Associates Inaunga mkono Torram ili Kuendeleza Fedha Iliyowekwa madarakani kwenye Bitcoin

Draper Associates Torramni Bitcoin-da markazlashmagan moliyalashtirishni qo'llab-quvvatlaydi

Uni-OVSeg: A Step Towards Efficient and Bias-Resilient Vision Systems

TORRANCE, United States / California, October 7th, 2024, CyberNewsWire/--Criminal IP, a renowned Cyber Threat Intelligence (CTI) search engine developed by AI SPERA, has partnered with Hybrid Analysis, a platform that provides advanced malware analysis and threat intelligence, to enhance threat research. This collaboration integrates Criminal IP’s advanced domain scanning capabilities into the Hybrid Analysis platform, providing security professionals with deeper insights and more effective threat mitigation strategies. Comprehensive Malware and Domain Analysis Hybrid Analysis employs dynamic and static techniques for thorough malware analysis. Real-time execution environments and memory dumps generate annotated disassembly listings and critical Indicators of Compromise (IOCs). Criminal IP specializes in real-time domain scanning, scrutinizing domains for phishing, malware, and illicit activities. Integration enriches threat profiles, improving threat detection accuracy. Key Benefits of the Collaboration: Enhanced Threat Profiling: Security professionals can gain deeper insights into the origins and behaviors of threats identified through Hybrid Analysis, enriched with Criminal IP's data.
Real-Time Domain Analysis: Integration with Criminal IP enables users to conduct real-time scans on domains of interest, which is crucial for accurately identifying emerging threats promptly.
Comprehensive Security Insights: Users gain access to detailed domain attributes such as phishing records, abuse incidents, and detection of embedded malicious code, enhancing their ability to analyze for signs of Domain Generation Algorithms (DGA) and phishing probabilities.
Interactive Score Card: Users can quickly assess domain status, accessing additional details directly from Criminal IP database to make informed decisions based on the latest threat intelligence. Criminal IP’s Advanced Real-Time Threat Detection In addition to this comprehensive maliciousness result, uses seeking information about each component and false positives can visit Criminal IP. The URL scan feature allows users to extract a wealth of data, including network logs, associated IP addresses, malicious links, and website vulnerabilities. Users of Criminal IP Domain Search can access valuable insights such as technology usage specifics, abuse records, and identified CVE vulnerabilities, all conveniently consolidated on a single page. This robust search engine offers three customizable subscription plans—Lite, Medium, and Pro—including a Free membership option. To determine the most suitable plan based on user's volume of IP Lookup and URL Scan/Lookup requirements, users can explore the Free membership, monitor their credit usage through a user-friendly dashboard, and take advantage of key features for gaining valuable insights. About AI SPERA AI SPERA, a leader in Cyber Threat Intelligence (CTI) solutions, significantly expanded its reach by launching its flagship solution, Criminal IP, in 2023. Since then, the company has formed technical and business collaborations with over 40 renowned global security firms, including Hybrid Analysis, VirusTotal, Cisco, Tenable, Sumo Logic, and Quad9. Besides the CTI search engine, the company offers Criminal IP ASM, a SaaS-based Attack Surface Management Solution on AWS Marketplace and Azure Marketplace, and Criminal IP FDS, an AI-based Anomaly Detection Solution for credential stuffing prevention and fraud detection. Available in five languages (English, French, Arabic, Korean, and Japanese), the search engine provides a powerful service for users worldwide. Contact Michael Sena AI SPERA support@aispera.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here. TORRANCE, United States / California, October 7th, 2024, CyberNewsWire/--Criminal IP, a renowned Cyber Threat Intelligence (CTI) search engine developed by AI SPERA, has partnered with Hybrid Analysis, a platform that provides advanced malware analysis and threat intelligence, to enhance threat research. TORRANCE, United States / California, October 7th, 2024, CyberNewsWire/--Criminal IP, This collaboration integrates Criminal IP’s advanced domain scanning capabilities into the Hybrid Analysis platform, providing security professionals with deeper insights and more effective threat mitigation strategies. Comprehensive Malware and Domain Analysis Hybrid Analysis employs dynamic and static techniques for thorough malware analysis. Real-time execution environments and memory dumps generate annotated disassembly listings and critical Indicators of Compromise (IOCs). Criminal IP specializes in real-time domain scanning, scrutinizing domains for phishing, malware, and illicit activities. Integration enriches threat profiles, improving threat detection accuracy. Key Benefits of the Collaboration: Enhanced Threat Profiling: Security professionals can gain deeper insights into the origins and behaviors of threats identified through Hybrid Analysis, enriched with Criminal IP's data. Real-Time Domain Analysis: Integration with Criminal IP enables users to conduct real-time scans on domains of interest, which is crucial for accurately identifying emerging threats promptly. Comprehensive Security Insights: Users gain access to detailed domain attributes such as phishing records, abuse incidents, and detection of embedded malicious code, enhancing their ability to analyze for signs of Domain Generation Algorithms (DGA) and phishing probabilities. Interactive Score Card: Users can quickly assess domain status, accessing additional details directly from Criminal IP database to make informed decisions based on the latest threat intelligence. Enhanced Threat Profiling: Security professionals can gain deeper insights into the origins and behaviors of threats identified through Hybrid Analysis, enriched with Criminal IP's data. Real-Time Domain Analysis: Integration with Criminal IP enables users to conduct real-time scans on domains of interest, which is crucial for accurately identifying emerging threats promptly. Comprehensive Security Insights: Users gain access to detailed domain attributes such as phishing records, abuse incidents, and detection of embedded malicious code, enhancing their ability to analyze for signs of Domain Generation Algorithms (DGA) and phishing probabilities. Interactive Score Card: Users can quickly assess domain status, accessing additional details directly from Criminal IP database to make informed decisions based on the latest threat intelligence. Criminal IP’s Advanced Real-Time Threat Detection In addition to this comprehensive maliciousness result, uses seeking information about each component and false positives can visit Criminal IP. The URL scan feature allows users to extract a wealth of data, including network logs, associated IP addresses, malicious links, and website vulnerabilities. Users of Criminal IP Domain Search can access valuable insights such as technology usage specifics, abuse records, and identified CVE vulnerabilities, all conveniently consolidated on a single page. Criminal IP Domain Search Criminal IP Domain Search This robust search engine offers three customizable subscription plans—Lite, Medium, and Pro—including a Free membership option. To determine the most suitable plan based on user's volume of IP Lookup and URL Scan/Lookup requirements, users can explore the Free membership, monitor their credit usage through a user-friendly dashboard, and take advantage of key features for gaining valuable insights. About AI SPERA AI SPERA , a leader in Cyber Threat Intelligence (CTI) solutions, significantly expanded its reach by launching its flagship solution, Criminal IP, in 2023. AI SPERA AI SPERA Since then, the company has formed technical and business collaborations with over 40 renowned global security firms, including Hybrid Analysis, VirusTotal, Cisco, Tenable, Sumo Logic, and Quad9. technical and business collaborations technical and business collaborations Besides the CTI search engine, the company offers Criminal IP ASM, a SaaS-based Attack Surface Management Solution on AWS Marketplace and Azure Marketplace, and Criminal IP FDS, an AI-based Anomaly Detection Solution for credential stuffing prevention and fraud detection. Available in five languages (English, French, Arabic, Korean, and Japanese), the search engine provides a powerful service for users worldwide. search engine search engine Contact Michael Sena AI SPERA support@aispera.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Hybrid Analysis Utilizes Criminal IP’s Robust Domain Data For Better Malware Detection

يستخدم التحليل الهجين بيانات المجال القوية لعناوين IP الإجرامية للكشف عن البرامج الضارة بشكل أفضل

Hibrid Təhlil Zərərli proqram təminatının daha yaxşı aşkarlanması üçün Cinayət IP-nin Güclü Domen Məlumatlarından istifadə edir

Η υβριδική ανάλυση χρησιμοποιεί τα ισχυρά δεδομένα τομέα της εγκληματικής IP για καλύτερη ανίχνευση κακόβουλου λογισμικού

El análisis híbrido utiliza datos de dominio robustos de IP criminales para una mejor detección de malware

L'analyse hybride utilise les données de domaine robustes de la propriété intellectuelle criminelle pour une meilleure détection des logiciels malveillants

ハイブリッド分析は犯罪者のIPの堅牢なドメインデータを活用してマルウェア検出を改善します

Гибридті талдау зиянды бағдарламаны жақсырақ анықтау үшін қылмыстық IP-нің сенімді домен деректерін пайдаланады

Análise Híbrida Utiliza Dados de Domínio Robustos de IP Criminoso para Melhor Detecção de Malware

Falanqaynta Isku-dhafka ah waxay adeegsataa Dembiyada IP ee xogta Domain adag si loo ogaado Malware ka wanaagsan

Хибридна анализа користи робусне податке домена криминалне ИП адресе за боље откривање злонамерног софтвера

Таҳлили гибридӣ маълумоти домени ҷиноии IP-ро барои беҳтар муайян кардани нармафзори зараровар истифода мебарад

Ukuhlaziywa Kwe-Hybrid Kusebenzisa Idatha Yesizinda Se-Criminal IP's Robust Ukuze Kutholwe I-Malware Engcono

**TORRANCE, United States / California, October 3rd, 2024/CyberNewsWire/--**An exclusive live webinar will take place on October 4th at noon Eastern Time (ET), demonstrating how Criminal IP’s Attack Surface Management (ASM) can help organizations proactively detect and mitigate cyber threats. The webinar will feature a Criminal IP ASM Live Demo, providing a comprehensive view of attack surface visibility. As cyber threats continue to evolve, businesses must strengthen their defenses, and this session will outline essential steps for achieving that goal. ▶ Users can register now for the free webinar at 12PM ET on October 10 ◀ Key Takeaways: Criminal IP ASM Live Demo: Seeing ASM in action as it uncovers hidden threats and helps secure user's organization attack surface.
Comprehensive Attack Surface Visibility: Discovering how to gain real-time insights into a user's organization’s digital ecosystem, identifying vulnerabilities before they can be exploited.
Proactive Threat Detection and Mitigation: Learning how to detect risks early and implement mitigation strategies to minimize exposure.
Strengthening Cyber Defenses with ASM: Exploring how ASM can be integrated into cybersecurity frameworks to enhance protection and provide continuous defense against threats. This webinar is designed for IT professionals, security managers, and decision-makers looking to advance their cybersecurity strategies and stay ahead in today’s rapidly changing threat landscape. Users can register now to gain valuable insights on safeguarding their organization's attack surface and taking proactive steps toward a more secure future. Contact Michael Sena AI SPERA support@aispera.com This story was distributed as a release by CyberNewsWire under HackerNoon’s Business Blogging Program. Learn more about the program here **TORRANCE, United States / California, October 3rd, 2024/CyberNewsWire/--**An exclusive live webinar will take place on October 4th at noon Eastern Time (ET), demonstrating how Criminal IP’s Attack Surface Management (ASM) can help organizations proactively detect and mitigate cyber threats. The webinar will feature a Criminal IP ASM Live Demo, providing a comprehensive view of attack surface visibility. As cyber threats continue to evolve, businesses must strengthen their defenses, and this session will outline essential steps for achieving that goal. Criminal IP ASM Criminal IP ASM ▶ Users can register now for the free webinar at 12PM ET on October 10 ◀ ▶ Users can register now for the free webinar at 12PM ET on October 10 ◀ ▶ Users can register now for the free webinar at 12PM ET on October 10 ◀ Key Takeaways: Criminal IP ASM Live Demo: Seeing ASM in action as it uncovers hidden threats and helps secure user's organization attack surface. Comprehensive Attack Surface Visibility: Discovering how to gain real-time insights into a user's organization’s digital ecosystem, identifying vulnerabilities before they can be exploited. Proactive Threat Detection and Mitigation: Learning how to detect risks early and implement mitigation strategies to minimize exposure. Strengthening Cyber Defenses with ASM: Exploring how ASM can be integrated into cybersecurity frameworks to enhance protection and provide continuous defense against threats. Criminal IP ASM Live Demo: Seeing ASM in action as it uncovers hidden threats and helps secure user's organization attack surface. Comprehensive Attack Surface Visibility: Discovering how to gain real-time insights into a user's organization’s digital ecosystem, identifying vulnerabilities before they can be exploited. Proactive Threat Detection and Mitigation: Learning how to detect risks early and implement mitigation strategies to minimize exposure. Strengthening Cyber Defenses with ASM: Exploring how ASM can be integrated into cybersecurity frameworks to enhance protection and provide continuous defense against threats. This webinar is designed for IT professionals, security managers, and decision-makers looking to advance their cybersecurity strategies and stay ahead in today’s rapidly changing threat landscape. Users can register now to gain valuable insights on safeguarding their organization's attack surface and taking proactive steps toward a more secure future. Users can register now Users can register now Contact Michael Sena AI SPERA support@aispera.com This story was distributed as a release by CyberNewsWire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by CyberNewsWire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Webinar: Attack Surface Management To The Rescue – Find, Fix, Fortify Your ASM With Criminal IP

Webinar: Aanvalsoppervlakbestuur tot die redding – Vind, maak reg, versterk jou ASM met kriminele IP

ندوة عبر الإنترنت: إدارة سطح الهجوم للإنقاذ - العثور على سطح الهجوم وإصلاحه وتعزيزه باستخدام الملكية الفكرية الإجرامية

Вэбінар: Attack Surface Management на дапамогу – знайдзіце, выпраўце, умацуйце свой ASM злачынным IP

Seminari web: Gestió de superfícies d'atac al rescat: trobeu, arregleu i enfortigueu el vostre ASM amb IP criminal

Seminario web: La gestión de la superficie de ataque al rescate: encuentre, repare y fortalezca su ASM con propiedad intelectual delictiva

ウェビナー: 攻撃サーフェス管理による救済 - 犯罪 IP による ASM の検出、修正、強化

Webinarium: Zarządzanie powierzchnią ataku na ratunek – znajdź, napraw, wzmocnij swój ASM za pomocą przestępczego IP

Webinar: Gerenciamento de superfície de ataque para o resgate – Encontre, conserte e fortaleça seu ASM com IP criminoso

Urubuga: Gutera Ubuyobozi bwa Surface Kubatabazi - Shakisha, Gukosora, Komeza ASM yawe hamwe na IP mpanabyaha

Webbseminarium: Attack Surface Management to the Rescue – Hitta, fixa, förstärk din ASM med kriminell IP

Вебінар: Attack Surface Management To The Rescue – Знайдіть, виправте, зміцніть свій ASM за допомогою злочинної IP-адреси

I-Webinar: Uhlaselo lwe-Surface Management ku-Rescue-Fumana, Lungisa, Yomeleza i-ASM yakho nge-IP yoLwaphulo-mthetho

TORRANCE, United States / California, September 11th, 2024/CyberNewsWire/--Criminal IP, a distinguished leader in Cyber Threat Intelligence (CTI) search engine developed by AI SPERA, announced that it has successfully integrated its IP address-related risk detection data with IPLocation.io, one of the most visited IP analysis and lookup tools on the internet. Through the integration, IPLocation.io, a prominent IP address geolocation tracker platform with a substantial user base, now offers more detailed insights on IP addresses from Criminal IP's accurate and up-to-date threat intelligence database. Innovative Data Ecosystem Driven by Search Engine and AI Expertise This is a groundbreaking advancement because Criminal IP’s database, built on a search engine framework, is more than a collection of information; it's a refined machine learning ecosystem honed through extensive scanning and detection of malicious IP addresses. The system continuously enhances its accuracy by transforming self-collected threat data, particularly behavioral patterns for IP address evasion, into actionable intelligence using AI and machine learning techniques. Tracking Down Threat History With Snort And Scanner Information Unlike traditional IP tracking methods, which only provide the geographic location of IP addresses, Criminal IP data in IPLocation.io now delivers information from ‘Snort’, a network intrusion detection system, and vulnerability scanners on open ports. This, along with newly provided inbound and outbound scores of IP addresses, enables the comprehensive identification of threat information related to IP addresses. Users can now compile the information themselves to assess the risk of an IP address, supported by a variety of fact-based evidence. Threat data from Criminal IP accessible on IPLocation.io website Among IP Location Lookup data sources, Criminal IP provides the most comprehensive data, offering 25 distinct data points for a single IP address. Evasion-Focused Dynamic Pattern Analysis Unveils Potential Attack Scenarios Users can also detect IP address obfuscation and protect their assets with new VPN, Proxy, and Tor data in IP Location. The aforementioned data helps identify discrepancies between actual and reported locations of an IP address, as well as traffic routed through multiple servers, thus revealing potential threats. The system goes beyond a mere review of past records; it offers predictive insights into future risks by analyzing behavioral patterns and potential attack scenarios. This underscores the CTI database’s distinctive capability to continuously learn and analyze attack patterns executed online. About AI SPERA AI SPERA equips cybersecurity professionals with advanced tools and insights to protect digital assets. Its flagship products include Criminal IP, renowned for its AI-based search engine, as well as attack surface management and fraud detection for enterprise solutions. The firm's established presence in major marketplaces like AWS and Snowflake further underscores its credibility and the trust placed in its services by leading industry players. Recently, AI SPERA achieved Level 1 certification under PCI DSS v4.0, overseen by the six leading global card issuers, showcasing its commitment to top-tier data security. Contact Michael Sena AI SPERA support@aispera.com This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. TORRANCE, United States / California, September 11th, 2024/CyberNewsWire/--Criminal IP, a distinguished leader in Cyber Threat Intelligence (CTI) search engine developed by AI SPERA, announced that it has successfully integrated its IP address-related risk detection data with IPLocation.io, one of the most visited IP analysis and lookup tools on the internet. Through the integration, IPLocation.io, a prominent IP address geolocation tracker platform with a substantial user base, now offers more detailed insights on IP addresses from Criminal IP's accurate and up-to-date threat intelligence database. Innovative Data Ecosystem Driven by Search Engine and AI Expertise This is a groundbreaking advancement because Criminal IP’s database, built on a search engine framework, is more than a collection of information; it's a refined machine learning ecosystem honed through extensive scanning and detection of malicious IP addresses. The system continuously enhances its accuracy by transforming self-collected threat data, particularly behavioral patterns for IP address evasion, into actionable intelligence using AI and machine learning techniques. Tracking Down Threat History With Snort And Scanner Information Unlike traditional IP tracking methods, which only provide the geographic location of IP addresses, Criminal IP data in IPLocation.io now delivers information from ‘Snort’, a network intrusion detection system, and vulnerability scanners on open ports. This, along with newly provided inbound and outbound scores of IP addresses, enables the comprehensive identification of threat information related to IP addresses. Users can now compile the information themselves to assess the risk of an IP address, supported by a variety of fact-based evidence. Threat data from Criminal IP accessible on IPLocation.io website Among IP Location Lookup data sources, Criminal IP provides the most comprehensive data, offering 25 distinct data points for a single IP address. Evasion-Focused Dynamic Pattern Analysis Unveils Potential Attack Scenarios Users can also detect IP address obfuscation and protect their assets with new VPN, Proxy, and Tor data in IP Location. The aforementioned data helps identify discrepancies between actual and reported locations of an IP address, as well as traffic routed through multiple servers, thus revealing potential threats. The system goes beyond a mere review of past records; it offers predictive insights into future risks by analyzing behavioral patterns and potential attack scenarios. This underscores the CTI database’s distinctive capability to continuously learn and analyze attack patterns executed online. About AI SPERA AI SPERA equips cybersecurity professionals with advanced tools and insights to protect digital assets. Its flagship products include Criminal IP, renowned for its AI-based search engine, as well as attack surface management and fraud detection for enterprise solutions. The firm's established presence in major marketplaces like AWS and Snowflake further underscores its credibility and the trust placed in its services by leading industry players. Recently, AI SPERA achieved Level 1 certification under PCI DSS v4.0, overseen by the six leading global card issuers, showcasing its commitment to top-tier data security. Contact Michael Sena AI SPERA support@aispera.com This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Criminal IP Teams Up with IPLocation.io To Deliver Unmatched IP Solutions To Global Audiences

Criminal IP се обединява с IPLocation.io, за да предостави несравними IP решения на глобалната публика

Criminal IP se udružuje sa IPLocation.io kako bi globalnoj publici pružio neusporediva IP rješenja

Criminal IP se asocia con IPLocation.io para ofrecer soluciones de propiedad intelectual inigualables a audiencias globales

IP פלילי משתף פעולה עם IPLocation.io כדי לספק פתרונות IP ללא תחרות לקהלים גלובליים

Criminal IP が IPLocation.io と提携し、比類のない IP ソリューションを世界中のユーザーに提供

IP ឧក្រិដ្ឋកម្មសហការជាមួយ IPLocation.io ដើម្បីផ្តល់ដំណោះស្រាយ IP ដែលមិនផ្គូផ្គងដល់ទស្សនិកជនសកល

Criminal IP가 IPLocation.io와 협력하여 글로벌 대상 고객에게 타의 추종을 불허하는 IP 솔루션 제공

IP ຄະດີອາຍາຮ່ວມກັບ IPLocation.io ເພື່ອສົ່ງການແກ້ໄຂ IP ທີ່ບໍ່ກົງກັນກັບຜູ້ຊົມທົ່ວໂລກ

Criminal IP се здружува со IPLocation.io за да испорача неспоредливи IP решенија до глобалната публика

Criminal IP werkt samen met IPLocation.io om ongeëvenaarde IP-oplossingen te leveren aan een wereldwijd publiek

جرمي IP د IPLocation.io سره یوځای کیږي ترڅو نړیوالو لیدونکو ته بې مثاله IP حلونه وړاندې کړي

Criminal IP ร่วมมือกับ IPLocation.io เพื่อนำเสนอโซลูชัน IP ที่ไม่มีใครเทียบได้ให้กับผู้ชมทั่วโลก

**Torrance, United States / California, September 4th, 2024/CyberNewsWire/--**AI SPERA, a leading Cyber Threat Intelligence (CTI) company, has achieved PCI DSS v4.0 certification for its flagship search engine solution, Criminal IP. This accomplishment builds on last year's attainment of PCI DSS v3.2.1 (Payment Card Industry Data Security Standard) certification and marks a significant milestone in the company's ongoing efforts to enhance security, further solidifying its leadership in the global market. The Payment Card Industry Data Security Standard (PCI DSS) is an international information security standard established by the PCI Security Standards Council (PCI SSC), which includes major global card brands. PCI DSS certification is a critical requirement in the financial industry for preventing credit card fraud and data breaches. Companies that earn this certification are verified as capable of securely managing customers' payment information. AI SPERA's newly acquired PCI DSS v4.0 certification represents the latest version of this standard, featuring enhanced security requirements that address emerging threats and technological advancements. Key assessment areas include maintaining secure networks and systems, protecting cardholder data, managing vulnerabilities and malware threats, enforcing strong access control measures, conducting regular monitoring and testing, and managing information security policies. Criminal IP has been rated at the highest level across all these criteria. The CTO of AI SPERA stated, "Achieving the highest level of PCI DSS v4.0 certification showcases our capabilities and commitment as a global security service provider. We will continue to prioritize protecting customer payment data while offering secure and trustworthy services." About AI SPERA AI SPERA, renowned for its advanced solutions, has expanded internationally, with 'Criminal IP' as its flagship offering. Operating in more than 150 countries, 'Criminal IP' is backed by enterprise-grade security solutions like 'Criminal IP ASM' and 'Criminal IP FDS'. Strategic partnerships with global leaders such as Cisco, VirusTotal, and Quad9 have significantly enhanced 'Criminal IP's capabilities. Recently, AI SPERA’s 'Criminal IP' has entered the marketplace of major US data warehousing platforms, including Amazon Web Services (AWS), Microsoft Azure, and Snowflake, expanding its global reach for threat data. Contact Michael Sena support@aispera.com This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **Torrance, United States / California, September 4th, 2024/CyberNewsWire/--**AI SPERA, a leading Cyber Threat Intelligence (CTI) company, has achieved PCI DSS v4.0 certification for its flagship search engine solution, Criminal IP. This accomplishment builds on last year's attainment of PCI DSS v3.2.1 (Payment Card Industry Data Security Standard) certification and marks a significant milestone in the company's ongoing efforts to enhance security, further solidifying its leadership in the global market. The Payment Card Industry Data Security Standard (PCI DSS) is an international information security standard established by the PCI Security Standards Council (PCI SSC), which includes major global card brands. PCI DSS certification is a critical requirement in the financial industry for preventing credit card fraud and data breaches. Companies that earn this certification are verified as capable of securely managing customers' payment information. AI SPERA's newly acquired PCI DSS v4.0 certification represents the latest version of this standard, featuring enhanced security requirements that address emerging threats and technological advancements. Key assessment areas include maintaining secure networks and systems, protecting cardholder data, managing vulnerabilities and malware threats, enforcing strong access control measures, conducting regular monitoring and testing, and managing information security policies. Criminal IP has been rated at the highest level across all these criteria. The CTO of AI SPERA stated, "Achieving the highest level of PCI DSS v4.0 certification showcases our capabilities and commitment as a global security service provider. We will continue to prioritize protecting customer payment data while offering secure and trustworthy services." About AI SPERA AI SPERA , renowned for its advanced solutions, has expanded internationally, with 'Criminal IP' as its flagship offering. Operating in more than 150 countries, ' Criminal IP ' is backed by enterprise-grade security solutions like 'Criminal IP ASM' and 'Criminal IP FDS'. Strategic partnerships with global leaders such as Cisco, VirusTotal, and Quad9 have significantly enhanced 'Criminal IP's capabilities. AI SPERA AI SPERA Criminal IP Criminal IP Recently, AI SPERA’s 'Criminal IP' has entered the marketplace of major US data warehousing platforms, including Amazon Web Services (AWS), Microsoft Azure, and Snowflake, expanding its global reach for threat data. Contact Michael Sena support@aispera.com This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Criminal IP Secures PCI DSS v4.0 Certification, Enhancing Payment Security With Top-Level Compliance

حماية الملكية الفكرية الجنائية من خلال شهادة PCI DSS v4.0، وتعزيز أمان الدفع من خلال الامتثال على أعلى مستوى

Η Criminal IP εξασφαλίζει την πιστοποίηση PCI DSS v4.0, ενισχύοντας την ασφάλεια πληρωμών με συμμόρφωση ανώτατου επιπέδου

Criminal IP obtiene la certificación PCI DSS v4.0 y mejora la seguridad de los pagos con un cumplimiento de alto nivel

IP Kriminèl Sekirize Sètifikasyon PCI DSS v4.0, Amelyore Sekirite Peman ak Konfòmite O-Nivo

Criminal IP が PCI DSS v4.0 認証を取得し、最高レベルのコンプライアンスで決済セキュリティを強化

Criminal IP უზრუნველყოფს PCI DSS v4.0 სერთიფიკატს, აძლიერებს გადახდის უსაფრთხოებას უმაღლესი დონის შესაბამისობით

आपराधिक आईपी सुरक्षित गर्दछ PCI DSS v4.0 प्रमाणीकरण, उच्च-स्तर अनुपालनको साथ भुक्तानी सुरक्षा बढाउँदै

Kryminalne IP zabezpiecza certyfikat PCI DSS v4.0, zwiększając bezpieczeństwo płatności dzięki zgodności na najwyższym poziomie

Huchasapa IP PCI DSS v4.0 Certificación kaqmanta Waqaychan, Pagay Waqaychayta Aswan Pata-Nivel hunt'aywan kallpachan

Dembiile IP waxa ay sugtaa Shahaadada PCI DSS v4.0, Kor u qaadida Ammaanka Lacag bixinta iyadoo la raacayo U hoggaansanaanta Heerka Sare

Sinisiguro ng Kriminal na IP ang PCI DSS v4.0 Certification, Pagpapahusay ng Seguridad sa Pagbabayad Gamit ang Nangungunang Antas na Pagsunod

Jinoiy IP PCI DSS v4.0 sertifikatini ta'minlaydi, to'lov xavfsizligini yuqori darajadagi muvofiqlik bilan oshiradi

CulturaX: A High-Quality, Multilingual Dataset for LLMs - Conclusion and References

NExT-GPT: Any-to-Any Multimodal LLM: Conclusion and References

Authors:
(1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic;
(2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic;
(3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic;
(4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Table of Links Abstract and Introduction ANN-Based Scene Classification Methodology Experiments Conclusion and Future Research, Acknowledgments and References 5 Conclusion and Future Research In this paper was provided an insight into the possibility of using artificial neural networks for scene recognition location from a video sequence with a small set of repeated shooting locations (such as in television series) was provided. Our idea was to select more than one frame from each scene and classify the scene using that sequence of frames. We used a pretrained VGG19 network without two last layers. This results were used as an input to the trainable part our neural network architecture. We have designed six neural network models with different layer types. We have investigated different neural network layers to combine video frames, in particular average-pooling, max-pooling, product, flatten, LSTM, and bidirectional LSTM layers. The considered networks have been tested and compared on a dataset obtained from The Big Bang Theory television series. The model with max-pooling layer was not successful, its accuracy was the lowest of all models. The models with a flatten or product layer were very unstable, their standard deviation was very large. The most stable among all models was the one with an average-pooling layer. The models with unidirectional LSTM and bidirectional LSTM had similar standard deviation of the accuracy. The model with a bidirectional LSTM had the highest accuracy among all considered models. In our opinion, this is because its internal memory cells preserve information in both directions. Those results shows, that models with internal memory are able to classify with a higher accuracy than models without internal memory. Our method may have limitations due to the chosen pretrained ANN and the low dimension of some neural layer parts. In future research, it is desirable to achieve higher accuracy in scene location recognition. This task may also need modifying model parameters or using other architectures. It also may need other pretrained models or combining several pretrained models. It is also desirable that, if the ANN detects an unknown scene, it will remember it and next time it will recognize a scene from the same location properly. Acknowledgments The research reported in this paper has been supported by the Czech Science Foundation (GACR) grant 18-18080S. Computational resources were supplied by the project "e-Infrastruktura CZ" (e-INFRA LM2018140) provided within the program Projects of Large Research, Development and Innovations Infrastructures. Computational resources were provided by the ELIXIRCZ project (LM2018131), part of the international ELIXIR infrastructure. References [1] Zhong, W., Kjellström, H.: Movie scene recognition with Convolutional Neural Networks. https://www.diva-portal.org/smash/get/diva2 :859486/FULLTEXT01.pdf KTH ROYAL INSTITUTE OF TECHNOLOGY (2015) 5–36 [2] Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. https://arxiv.org/pdf/1409.1556v6.pdf Visual Geometry Group, Department of Engineering Science, University of Oxford (2015) [3] Russakovsky O., Deng J., Hao S., Krause J., Satheesh S., Ma Sean, Huang Z., Karpathy A., Khosla A., Bernstein M., Berg A. C., Fei-Fei L.: ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision 115 (2015), pp. 211–252 [4] Li-jia L., Hao S., Fei-fei L., Xing E.: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification. https://cs.stanford.edu/groups/vision/pdf/ LiSuXingFeiFeiNIPS2010.pdf NIPS (2010) [5] Felix A. Gers, Schmidhuber J., and Cummins F., Learning to forget: Continual prediction with LSTM, in Proceedings of ICANN. ENNS (1999), pp. 850-–855 [6] Benavoli A., Corani G., Mangili F.: Should We Really Use Post-Hoc Tests Based on Mean-Ranks? Journal of Machine Learning Research 17 (2016), pp. 1–10 [7] García S., Herrera F.: An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons Journal of Machine Learning Research 9 (2008), pp. 2677–2694 [8] Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks. Springer (2012) [9] Kaiming H., Xiangyu Z., Shaoqing R., Jian S.: Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778 [10] Sudha V., Ganeshbabu T. R.: A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning. http://www.techscience.com/cmc/v66n1/40483 Computers, Materials & Continua (2021), pp. 827–842 [11] Zhou B., Lapedriza A., Khosla A., Oliva A., Torralba A.: Places: A 10 Million Image Database for Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (2018), pp. 1452–1464 This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; (4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Authors: Authors: (1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; (4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Table of Links Abstract and Introduction Abstract and Introduction ANN-Based Scene Classification ANN-Based Scene Classification Methodology Methodology Experiments Experiments Conclusion and Future Research, Acknowledgments and References Conclusion and Future Research, Acknowledgments and References 5 Conclusion and Future Research In this paper was provided an insight into the possibility of using artificial neural networks for scene recognition location from a video sequence with a small set of repeated shooting locations (such as in television series) was provided. Our idea was to select more than one frame from each scene and classify the scene using that sequence of frames. We used a pretrained VGG19 network without two last layers. This results were used as an input to the trainable part our neural network architecture. We have designed six neural network models with different layer types. We have investigated different neural network layers to combine video frames, in particular average-pooling, max-pooling, product, flatten, LSTM, and bidirectional LSTM layers. The considered networks have been tested and compared on a dataset obtained from The Big Bang Theory television series. The model with max-pooling layer was not successful, its accuracy was the lowest of all models. The models with a flatten or product layer were very unstable, their standard deviation was very large. The most stable among all models was the one with an average-pooling layer. The models with unidirectional LSTM and bidirectional LSTM had similar standard deviation of the accuracy. The model with a bidirectional LSTM had the highest accuracy among all considered models. In our opinion, this is because its internal memory cells preserve information in both directions. Those results shows, that models with internal memory are able to classify with a higher accuracy than models without internal memory. Our method may have limitations due to the chosen pretrained ANN and the low dimension of some neural layer parts. In future research, it is desirable to achieve higher accuracy in scene location recognition. This task may also need modifying model parameters or using other architectures. It also may need other pretrained models or combining several pretrained models. It is also desirable that, if the ANN detects an unknown scene, it will remember it and next time it will recognize a scene from the same location properly. Acknowledgments The research reported in this paper has been supported by the Czech Science Foundation (GACR) grant 18-18080S. Computational resources were supplied by the project "e-Infrastruktura CZ" (e-INFRA LM2018140) provided within the program Projects of Large Research, Development and Innovations Infrastructures. Computational resources were provided by the ELIXIRCZ project (LM2018131), part of the international ELIXIR infrastructure. References [1] Zhong, W., Kjellström, H.: Movie scene recognition with Convolutional Neural Networks. https://www.diva-portal.org/smash/get/diva2 :859486/FULLTEXT01.pdf KTH ROYAL INSTITUTE OF TECHNOLOGY (2015) 5–36 [2] Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. https://arxiv.org/pdf/1409.1556v6.pdf Visual Geometry Group, Department of Engineering Science, University of Oxford (2015) [3] Russakovsky O., Deng J., Hao S., Krause J., Satheesh S., Ma Sean, Huang Z., Karpathy A., Khosla A., Bernstein M., Berg A. C., Fei-Fei L.: ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision 115 (2015), pp. 211–252 [4] Li-jia L., Hao S., Fei-fei L., Xing E.: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification. https://cs.stanford.edu/groups/vision/pdf/ LiSuXingFeiFeiNIPS2010.pdf NIPS (2010) [5] Felix A. Gers, Schmidhuber J., and Cummins F., Learning to forget: Continual prediction with LSTM, in Proceedings of ICANN. ENNS (1999), pp. 850-–855 [6] Benavoli A., Corani G., Mangili F.: Should We Really Use Post-Hoc Tests Based on Mean-Ranks? Journal of Machine Learning Research 17 (2016), pp. 1–10 [7] García S., Herrera F.: An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons Journal of Machine Learning Research 9 (2008), pp. 2677–2694 [8] Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks. Springer (2012) [9] Kaiming H., Xiangyu Z., Shaoqing R., Jian S.: Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778 [10] Sudha V., Ganeshbabu T. R.: A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning. http://www.techscience.com/cmc/v66n1/40483 Computers, Materials & Continua (2021), pp. 827–842 [11] Zhou B., Lapedriza A., Khosla A., Oliva A., Torralba A.: Places: A 10 Million Image Database for Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (2018), pp. 1452–1464 This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

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