Cowrie is a medium interaction SSH and Telnet honeypot designed to log brute force attacks and shell interaction performed by an attacker. Cowrie also functions as an SSH and telnet proxy to observe attacker behavior to another system. Cowrie was developed from Kippo. == Reception == Cowrie has been referenced in published papers. The Book "Hands-On Ethical Hacking and Network Defense" includes Cowrie in a list of 5 commercial honeypots. === Prior uses === Discussing a honeypot effort called the Project Heisenberg Cloud by Rapid7, Bob Rudis, the company's chief data scientist, told eWEEK, "There are custom Rapid7-developed low- and medium-interaction honeypots used within the framework, along with open-source ones, such as Cowrie." Doug Rickert has experimented with the open-source Cowrie SSH honeypot and wrote about it on Medium. Putting up a simple honeypot isn't difficult, and there are many open-source products besides Cowrie, including the original Honeyd to MongoDB and NoSQL honeypots, to ones that emulate web servers. Some appear to be SCADA or other more advanced applications. === Best practices === Researchers at the SysAdmin, Audit, Network and Security (SANS) institute urged administrators and security researchers to run the latest version of Cowrie on a honeypot to monitor shifts in the type of passwords being scanned for and pattern of attacks on IoT devices. === Discussion and further resources === Attack Detection and Forensics Using Honeypot in an IoT Environment calls Cowrie a "medium interaction honeypot" and describes results from using it for 40 days to capture "all communicated sessions in log files." The book Advances on Data Science also devotes chapter two to "Cowrie Honeypot Dataset and Logging." ICCWS 2018 13th International Conference on Cyber Warfare and Security describes using Cowrie. On the Move to Meaningful Internet Systems: OTM 2019 Conferences includes details of using Cowrie. Splunk, a security tool that can receive information from honeypots, outlines how to set up a honeypot using the open-source Cowrie package.
EnQuire
Enquire is a web-based software application used as a platform for project, contract and grant management, as well as reporting and planning. Initially designed for the specific business requirements of the Australian Government, Queensland Government and Queensland Regional Bodies to manage natural resource projects, Enquire has since seen adoption outside of this industry and user segment. The use of Enquire by Natural Resource Management bodies within Queensland has been cited as a reason for the improved efficiency, quantity and quality of reporting. Technically, Enquire is implemented as a Java application built on a MySQL database. Enquire is hosted and supported under the software as a service model by Tactiv Pty Ltd. == History == The system was first released in 2005 under the name ViSTA NRM Online, proactively changing its name to Enquire in 2007 to avoid possible confusion with Windows Vista, which was being released at the time. In 2012, the Enquire project and support team was commercialized as its own company called Tactiv Pty Ltd. Tactiv is based predominantly in Brisbane, Australia. Tactiv has continued to develop and grow the Enquire Grant, Contract and Project management solution, releasing a new platform in 2017. Since commercialization, Tactiv has grown its client base to include government and non-government organizations such as foundations and not-for-profit organizations. == Functionality == The functionality of Enquire can be broken down into 5 key lifecycle solutions, all fully integrated and supported by over 40 feature rich and configurable modules: Grant Management Contract Management Project Portfolio Management Procurement Management Relationship Management The system provides its platform to meet the needs of "off the shelf" customers looking for a ready to use best practice option as well as a fully configurable option for specific requirements. The system offers a client supplier portal for external applicants or suppliers, a management portal for internal team usage and an administration portal for clients to manage access, roles, information, and other configurations. Key functional modules include: Online authoring and publishing for forms and applications Workflows Project Tracking Performance Reporting Financial Reporting Stakeholder Communication Budget management Document Management Milestone tracking Payments and Variations Management KPI tracking and Impact reporting The Enquire system is used to report against the Queensland Government's Q2 Coast and Country Program and parts of the Australian Government's Caring for our Country program. There is also a strategic planning module, which provides functionality to manage core-business administration and reporting requirements, whilst providing visibility of key activities and their alignment against organizational goals and strategic objectives. The systems architecture supports a range of implementation models with the capacity to manage one-to-one, one-to-many and many-to-many relationships between investors and investees. Under the usage model within Queensland, Regional Bodies use Enquire to load project contracts and report against these online. The regional bodies also record output, target and financial information in Enquire, which can then be used for operational purposes including financial, performance and target reporting. == External Audit == The Australian National Audit Office Audit Report No.21 2007–08 undertook a case study on Enquire. It noted: "The Queensland Department of Environment and Resource Management has developed the first integrated web-based system [Enquire] to manage performance information about Natural Resource Management activities in Queensland." Four of Queensland's 14 regional bodies commented on Enquire through the ANAO's survey. These four regional bodies indicated that Enquire offers a means of consistent reporting at the State level.
Chainer
Chainer is an open source deep learning framework written purely in Python on top of NumPy and CuPy Python libraries. The development is led by Japanese venture company Preferred Networks in partnership with IBM, Intel, Microsoft, and Nvidia. Chainer is notable for its early adoption of "define-by-run" scheme, as well as its performance on large scale systems. The first version was released in June 2015 and has gained large popularity in Japan since then. Furthermore, in 2017, it was listed by KDnuggets in top 10 open source machine learning Python projects. In December 2019, Preferred Networks announced the transition of its development effort from Chainer to PyTorch and it will only provide maintenance patches after releasing v7. == Define-by-run == Chainer was the first deep learning framework to introduce the define-by-run approach. The traditional procedure to train a network was in two phases: define the fixed connections between mathematical operations (such as matrix multiplication and nonlinear activations) in the network, and then run the actual training calculation. This is called the define-and-run or static-graph approach. Theano and TensorFlow are among the notable frameworks that took this approach. In contrast, in the define-by-run or dynamic-graph approach, the connection in a network is not determined when the training is started. The network is determined during the training as the actual calculation is performed. One of the advantages of this approach is that it is intuitive and flexible. If the network has complicated control flows such as conditionals and loops, in the define-and-run approach, specially designed operations for such constructs are needed. On the other hand, in the define-by-run approach, programming language's native constructs such as if statements and for loops can be used to describe such flow. This flexibility is especially useful to implement recurrent neural networks. Another advantage is ease of debugging. In the define-and-run approach, if an error (such as numeric error) has occurred in the training calculation, it is often difficult to inspect the fault, because the code written to define the network and the actual place of the error are separated. In the define-by-run approach, you can just suspend the calculation with the language's built-in debugger and inspect the data that flows on your code of the network. Define-by-run has gained popularity since the introduction by Chainer and is now implemented in many other frameworks, including PyTorch and TensorFlow. == Extension libraries == Chainer has four extension libraries, ChainerMN, ChainerRL, ChainerCV and ChainerUI. ChainerMN enables Chainer to be used on multiple GPUs with performance significantly faster than other deep learning frameworks. A supercomputer running Chainer on 1024 GPUs processed 90 epochs of ImageNet dataset on ResNet-50 network in 15 minutes, which is four times faster than the previous record held by Facebook. ChainerRL adds state of art deep reinforcement learning algorithms, and ChainerUI is a management and visualization tool. == Applications == Chainer is used as the framework for PaintsChainer, a service which does automatic colorization of black and white, line only, draft drawings with minimal user input.
National Security Memorandum on Artificial Intelligence
The Memorandum on Advancing the United States' Leadership in Artificial Intelligence; Harnessing Artificial Intelligence to Fulfill National Security Objectives; and Fostering the Safety, Security, and Trustworthiness of Artificial Intelligence is a memorandum signed by U.S. president Joe Biden. The memorandum is described as seeking to advance U.S. leadership in the development of safe, secure, and trustworthy artificial intelligence (AI); enable the U.S. government to use AI for national security; and contribute to international AI governance.
International Journal on Artificial Intelligence Tools
The International Journal on Artificial Intelligence Tools was founded in 1992 and is published by World Scientific. It covers research on artificial intelligence (AI) tools, including new architectures, languages and algorithms. Topics include AI in Bioinformatics, Cognitive Informatics, Knowledge-Based/Expert Systems and Object-Oriented Programming for AI. == Abstracting and indexing == The journal is abstracted and indexed in: Inspec Science Citation Index Expanded ISI Alerting Services CompuMath Citation Index Current Contents/Engineering, Computing, and Technology
Flo (app)
Flo is a period-tracking app that provides menstrual cycle, ovulation and pregnancy tracking as well as perimenopause symptom tracking that was developed by Flo Health, Inc. It has over 380 million downloads worldwide and over 70 million monthly active users as of November 2024. In mid-2024, it reached unicorn status, and became Europe’s first femtech unicorn. The company has been accused of sharing users' sensitive health data with third parties without consent and misleading its users about data practices. == History == Flo Health, Inc. was co-founded in 2015 by Dmitry and Yuri Gurski, in Belarus. Their backgrounds helped build the first version of the software having experience in other fitness and health apps. Dmitry serves as the company's CEO. The company's development hubs are in London, Amsterdam and Vilnius. In 2016, the company raised $1 million in seed round funding from Flint Capital and Haxus Venture Fund. In 2017, Flo received an investment of $5 million from Flint Capital and model Natalia Vodianova with Vodianova helping develop an awareness campaign for the company. In 2018, Flo received an investment of $6 million from Mangrove Capital Partners, with participation from Flint Capital and Haxus, giving the company a valuation of $200 million. In mid-2019, Flo received an additional investment of $7.5 million led by Founders Fund. In 2020, the Federal Trade Commission alleged that Flo had misled users about its handling of health information to third parties including Google, Facebook, AppsFlyer, and Flurry since 2016. These allegations followed a 2019 report by The Wall Street Journal in reference to Facebook. The company reached a settlement in 2021 and was required to notify users of how their personal information was shared and obtain permission before any further information was shared. The agreement also required that Flo to undertake an independent privacy audit which it completed in March 2022. In early September 2021, Flo announced it closed $50M in a Series B financing, bringing the total capital raised to $65 million and company valuation to $800M led by VNV Global and Target Global. In March 2024, the Supreme Court of British Columbia certified a class action suit against Flo for sharing intimate data with Facebook and other third parties without user knowledge. In July 2024, Flo announced it raised more than $200M in Series C financing from General Atlantic bringing its valuation beyond $1 billion. As of November 2024, the app had over 380 million downloads world wide, and over 70 million monthly active users. In 2025, Flo adopted a data intelligence platform from Databricks to power its analytics and AI features, allowing users personalized cycle predictions. In 2025, a class action lawsuit in California was settled for $56 million with Flo paying $8 million and Google paying $48 million. == Features and privacy == Flo was initially created as a period and ovulation tracking application. It now provides reminders of upcoming menstrual cycles and a place to record various other health symptoms such as contraceptive methods, vaginal discharge (leukorrhea), water intake, pains, mood swings, and sexual activity. The application is available on iOS and Android. Flo is free to download and the free basic version gives you access to period and ovulation tracking and predictions, symptom tracking, cycle history, and anonymous mode. In Pregnancy mode, the app provides tracking features and educational material for pregnancy. In October 2023, Flo launched Flo for Partners, a feature that allows users to share their Flo data with their partner. In September 2022, as a response to Roe v. Wade being overturned, Flo sped up the release of a feature called "Anonymous Mode". Flo said this mode allows users to access the app without any personal identifiers such as name, email address, or technical identifiers being associated with their health data. Flo said it uses a technology called Oblivious HTTP to help protect user privacy in Anonymous Mode. == Recognition == Flo was named to Bloomberg’s Top 25 UK Startups to Watch for 2024. Flo's Anonymous Mode feature was recognized on both Fast Company's World Changing Ideas 2023 and TIME's Best Inventions List 2023. Flo is a CES 2019 Innovation Awards Honoree in the Software and Mobile Applications category.
GuideGeek
GuideGeek is an AI-powered travel assistant that was launched by travel publisher Matador Network in April 2023 and is accessed by users through Instagram, WhatsApp and Facebook Messenger to plan itineraries or provide travel tips and recommendations. It uses generative artificial intelligence technology from OpenAI. Matador Network is a San Francisco-based digital media company and online travel publication with millions of monthly visitors and social media followers. == Features == Users message GuideGeek questions about travel and receive customized answers and itineraries that are pulled from ChatGPT in addition to over 1,000 additional travel-specific integrations such as live flight, hotel and vacation rental data. Travelers can specify their budget and needs to generate custom itineraries. GuideGeek is not an app and does not require the user to download anything, instead relying on messaging apps such as Instagram to connect users with the AI. GuideGeek is free to use, doesn't include ads, and doesn't sell user data. Matador Network has a team of staff members monitoring conversations to correct them if the AI makes a false statement; for example, one user incorrectly inputted “Crete Freeze” instead of “Crete, Greece”, and the AI made up a fictional soft serve company. Using a technique known as reinforcement learning from human feedback (RLHF), the accuracy of GuideGeek increased to 98%, according to Matador Network CEO, Ross Borden. == Destination partnerships == Matador Network is monetizing GuideGeek via white-label partnerships with tourism bureaus and destination marketing organizations (DMOs). As of March 2024, it had over a dozen such clients. Estes Park, Colorado, was one of the first DMOs to partner with Matador for a custom version of GuideGeek called “Rocky Mountain Roamer.” For Discover Greece, Matador created Pythia, a custom AI named after the high priestess of the Temple of Apollo at Delphi. As Borden explained to Travel + Leisure, “Visitors to the Discover Greece website will find Pythia in the bottom right corner, and they can converse with the AI like a friend who knows everything about Greece.” Other DMOs who have partnerships with GuideGeek include the Aruba Tourism Authority, Visit Reno Tahoe, Illinois Office of Tourism, and Tourism Richmond. == Awards == In recognition of GuideGeek, Fast Company named Matador Network to its 2024 list of Most Innovative Companies. Following growth driven by the launch of GuideGeek, Matador Network was ranked on the 2024 Inc. 5000 list of fastest-growing private companies in America. The 2024 Skift IDEA Awards recognized Matador Network as a finalist in the category of Best Use of AI for GuideGeek's customized AI for the travel industry. == Michael Motamedi experiment == Travel influencer and chef Michael Motamedi traveled the world with his wife Vanessa Salas and their 2-year-old daughter on a six-month trip (which was later extended to a full year) led by GuideGeek. The family started off in Morocco before heading to Spain and continuing east. The experiment became the basis of a web series called “No Fixed Address.” Motamedi used GuideGeek's AI to select countries the family visited, where they ate, and what sites they saw. Motamedi and Salas first tested out the technology in April 2023 while using the chatbot to plan a date night in Mexico City. GuideGeek provided speakeasy and drink recommendations as well as local history facts.