Negobot also referred to as Lolita or Lolita chatbot is a chatterbot that was introduced to the public in 2013, designed by researchers from the University of Deusto and Optenet to catch online pedophiles. It is a conversational agent that utilizes natural language processing (NLP), information retrieval (IR) and Automatic Learning. Because the bot poses as a young female in order to entice and track potential predators, it became known in media as the "virtual Lolita", in reference to Vladimir Nabokov's novel. == Background == In 2013, the University of Deusto researchers published a paper on their work with Negobot and disclosed the text online. In their abstract, the researchers addressed the issue that an increasing number of children are using the internet and that these young users are more susceptible to existing internet risks. Their main objective was to create a chatterbot with the ability to trap online predators that posed a threat to children. They intended to deploy the bot into sites frequented by predators such as social networks and chatrooms. The university researchers used information provided by anti-pedophilia activist organization Perverted-Justice, including examples of online encounters and conversations with sexual predators, to supplement the program's artificial intelligence system. == Features == === Programmed persona === The chatterbot takes the guise of a naive and vulnerable 14-year-old girl. The bot's programmers used methods of artificial intelligence and natural language processing to create a conversational agent fluent in typical teenage slang, misspellings, and knowledge of pop culture. Through these linguistic features, the bot is able to mimic the conversational style of young teenagers. It also features split personalities and seven different patterns of conversation. Negobot's primary creator, Dr. Carlos Laorden, expressed the significance of the bot's distinguishable style of communication, stating that normally, "chatbots tend to be very predictable. Their behavior and interest in a conversation are flat, which is a problem when attempting to detect untrustworthy targets like paedophiles." What makes Negobot different is its game theory feature, which makes it able to "maintain a much more realistic conversation." Apart from being able to imitate a stereotypical teenager, the program is also able to translate messages into different languages. === Game theory === Negobot's designers programmed it with the ability to treat conversations with potential predators as if it were a game, the objective being to collect as much information on the suspect as possible that could provide evidence of pedophilic characteristics and motives. The use of game theory shapes the decisions the bot makes and the overall direction of the conversation. The bot initiates its undercover operations by entering a chat as a passive participant, waiting to be chatted by a user. Once a user elicits conversation, the bot will frame the conversation in such a way that keeps the target engaged, extracting personal information and discouraging it from leaving the chat. The information is then recorded to be potentially sent to the police. If the target seems to lose interest, the bot attempts to make it feel guilty by expressing sentiments of loneliness and emotional need through strategic, formulated responses, ultimately prolonging interaction. In addition, the bot may provide fake information about itself in attempt to lure the target into physical meetings. === Limitations === Despite being able to carry out a realistic conversation, Negobot is still unable to detect linguistic subtleties in the messages of others, including sarcasm. == Controversy == John Carr, a specialist in online child safety, expressed his concern to BBC over the legality of this undercover investigation. He claimed that using the bot on unsuspecting internet users could be considered a form of entrapment or harassment. The type of information that Negobot collects from potential online predators, he said, is unlikely to be upheld in court. Furthermore, he warned that relying on only software without any real-world policing risks enticing individuals to do or say things that they would not have if real-world policing were a factor.
Secure environment
In computing, a secure environment is any system which implements the controlled storage and use of information. In the event of computing data loss, a secure environment is used to protect personal or confidential data. It may also be known as a trusted execution environment (TEE). Often, secure environments employ cryptography as a means to protect information. This is typically used for processing confidential or restricted information. Some secure environments employ cryptographic hashing, simply to verify that the information has not been altered since it was last modified.
Continuous Function Chart
A Continuous Function Chart (CFC) is a graphic editor that can be used in conjunction with the STEP 7 software package or with other tools, such as CODESYS. It is used to create the entire software structure of the CPU from ready-made blocks. When working with the editor, you place blocks on function charts, assign parameters to them, and interconnect them. Interconnecting means, for example, that values are transferred from one output to one or more inputs during communication between the blocks. Continuous function charts are basically used for controlling continuous processes, where all the logic is executed and outputs are calculated in each PLC scan. Whereas in SFC, execution will be sequential as done is batch processes.
Qlone
Qlone is a 3D scanning app based on photogrammetry for creation of 3D models on mobile devices. The resultant 3D models can be exported for external use. Qlone was featured at the Apple Worldwide Developers Conference in 2021. It was also featured on BBC Click. == Qlone features == === 3D scanning === 3D scanning with Qlone requires the use of an included mat design. The user prints the mat onto a sheet of paper, then places the object to be scanned in the centre of the mat. An augmented reality dome within the Qlone app guides the user through the subsequent scanning process. The iOS version of Qlone allows scanning without the mat. === 3D editing === Qlone's editing features allow users to adjust 3D scanned models using texture mapping, polygon mesh size simplification, digital sculpting, cleaning and smoothing, and artistic effects. === File export === Qlone exports directly to multiple 3D platforms including SketchFab, i.materialise, Lens Studio for Snapchat, Shapeways and CGTrader. Models can also be exported in different 3D formats for use in other 3D tools – OBJ, STL, FBX, USDZ, GLB (Binary gLTF), PLY, and X3D. == Use in Science, Education and Academia == Due to its inexpensive, simple and accessible nature for creating 3D models, Qlone was used in many academically educational and scientific research projects. The European Space Agency used Qlone to scan rocks in a Tele-Robotic rock collection experiment. Neurosurgeons from the University of Southern California and surgeons from Tulane University School of Medicine used Qlone to create 3D models of cadaveric specimens and anatomical models with the aim of increasing access to such components for enhancing anatomy training and allowing realistic surgical simulations for neurosurgeons and practitioners worldwide. Archaeologists from Texas A&M University used Qlone to create 3D replicas of artifacts and models and students from Vancouver iTech Preparatory Middle School used Qlone to create 3D scans of more than 100 artifacts from Fort Vancouver National Historic Site.
Articulatory speech recognition
Articulatory speech recognition means the recovery of speech (in forms of phonemes, syllables or words) from acoustic signals with the help of articulatory modeling or an extra input of articulatory movement data. Speech recognition (or automatic speech recognition, acoustic speech recognition) means the recovery of speech from acoustics (sound wave) only. Articulatory information is extremely helpful when the acoustic input is in low quality, perhaps because of noise or missing data. Measurable information from the articulatory system (e.g. tongue, jaw movements) can supplement acoustic signals to improve phone recognition accuracy by 2%. However, attempts to estimate articulatory data from acoustic signals alone have not significantly enhanced recognition performance.
Cognitive robotics
Cognitive robotics or cognitive technology is a subfield of robotics concerned with endowing a robot with intelligent behavior by providing it with a processing architecture that will allow it to learn and reason about how to behave in response to complex goals in a complex world. Cognitive robotics may be considered the engineering branch of embodied cognitive science and embodied embedded cognition, consisting of robotic process automation, artificial intelligence, machine learning, deep learning, optical character recognition, image processing, process mining, analytics, software development and system integration. == Core issues == While traditional cognitive modeling approaches have assumed symbolic coding schemes as a means for depicting the world, translating the world into these kinds of symbolic representations has proven to be problematic if not untenable. Perception and action and the notion of symbolic representation are therefore core issues to be addressed in cognitive robotics. == Starting point == Cognitive robotics views human or animal cognition as a starting point for the development of robotic information processing, as opposed to more traditional artificial intelligence techniques. Target robotic cognitive capabilities include perception processing, attention allocation, anticipation, planning, complex motor coordination, reasoning about other agents and perhaps even about their own mental states. Robotic cognition embodies the behavior of intelligent agents in the physical world (or a virtual world, in the case of simulated cognitive robotics). Ultimately, the robot must be able to act in the real world. == Learning techniques == === Motor Babble === A preliminary robot learning technique called motor babbling involves correlating pseudo-random complex motor movements by the robot with resulting visual and/or auditory feedback such that the robot may begin to expect a pattern of sensory feedback given a pattern of motor output. Desired sensory feedback may then be used to inform a motor control signal. This is thought to be analogous to how a baby learns to reach for objects or learns to produce speech sounds. For simpler robot systems, where, for instance, inverse kinematics may feasibly be used to transform anticipated feedback (desired motor result) into motor output, this step may be skipped. === Imitation === Once a robot can coordinate its motors to produce a desired result, the technique of learning by imitation may be used. The robot monitors the performance of another agent and then the robot tries to imitate that agent. It is often a challenge to transform imitation information from a complex scene into a desired motor result for the robot. Note that imitation is a high-level form of cognitive behavior and imitation is not necessarily required in a basic model of embodied animal cognition. === Knowledge acquisition === A more complex learning approach is "autonomous knowledge acquisition": the robot is left to explore the environment on its own. A system of goals and beliefs is typically assumed. A somewhat more directed mode of exploration can be achieved by "curiosity" algorithms, such as Intelligent Adaptive Curiosity or Category-Based Intrinsic Motivation. These algorithms generally involve breaking sensory input into a finite number of categories and assigning some sort of prediction system (such as an artificial neural network) to each. The prediction system keeps track of the error in its predictions over time. Reduction in prediction error is considered learning. The robot then preferentially explores categories in which it is learning (or reducing prediction error) the fastest. == Other architectures == Some researchers in cognitive robotics have tried using architectures such as (ACT-R and Soar (cognitive architecture)) as a basis of their cognitive robotics programs. These highly modular symbol-processing architectures have been used to simulate operator performance and human performance when modeling simplistic and symbolized laboratory data. The idea is to extend these architectures to handle real-world sensory input as that input continuously unfolds through time. What is needed is a way to somehow translate the world into a set of symbols and their relationships. == Questions == Some of the fundamental questions to be answered in cognitive robotics are: How much human programming should or can be involved to support the learning processes? How can one quantify progress? Some of the adopted ways are reward and punishment. But what kind of reward and what kind of punishment? In humans, when teaching a child, for example, the reward would be candy or some encouragement, and the punishment can take many forms. But what is an effective way with robots?
G'MIC
G'MIC (GREYC's Magic for Image Computing) is a free and open-source framework for image processing. It defines a script language that allows the creation of complex macros. Originally usable only through a command line interface, it is currently mostly popular as a GIMP plugin, and is also included in Krita. G'MIC is dual-licensed under CECILL-2.1 or CECILL-C. == Features == G'MIC's graphical interface is notable for its noise removal filters, which came from an earlier project called GREYCstoration by the same authors. G'MIC offers many built-in commands for image processing, including basic mathematical manipulations, look up tables, and filtering operations. More complex macros and pipelines built out of those commands are defined in its library files. == Interpreters == === Command line === G'MIC is primarily a script language callable from a shell. For example, to display an image: This command displays the image contained in the file image.jpg and allows zooming in to examine values. Several filters can be applied in succession. For example, to crop and resize an image: === Graphical interface === G'MIC comes with a Qt-based graphical interface, which may be integrated as a Gimp or Krita plugin. It contains several hundred filters written in the G'MIC language, dynamically updated through an internet feed. The interface provides a preview and setting sliders for each filter. G'MIC is one of the most popular Gimp plugins. === G'MIC Online === Most of the filters available for the graphical interface are also available online. === ZArt === ZArt is a graphical interface for real-time manipulation of webcam images. === libgmic === Libgmic is a C++ library that can be linked to third-party applications. It sees integration in Flowblade and Veejay.