Process Mining (PM) is a consolidated discipline grounded on data mining and business process management. The exploitation of traditional PM tasks (discovery, conformance checking, and enhancement) is today a reality in many organizations [1, 2]. In the last decade, a wave of new results in artificial intelligence has triggered the interest of the PM research community in using supervised or unsupervised Machine Learning (ML) techniques for gaining insight into business processes and providing advice on how to improve their inefficiencies. In today's practice, ML models are routinely integrated into PM data pipelines [3] to carry out tasks like data transformation, noise reduction, anomaly detection, classification, and prediction. For example, ML is playing a key role in the interface between PM and sensor platforms. Advances in sensing technologies have made it possible to deploy distributed monitoring platforms capable of detecting fine-grained events. The granularity gap between these events and the activities considered by classic PM analysis has often been bridged using ML models [4, 5] that compute virtual activity logs, a problem which is also known as log lifting [6].

The purpose of this work is to find out how different library classification systems and linguistic ontologies arrange a particular domain of interest and what are the limitations for information retrieval. We use knowledge representation techniques and languages for construction of a domain specific ontology. This ontology would help not only in problem solving, but it would demonstrate the ease with which complex queries can be handled using principles of domain ontology, thereby facilitating better information retrieval. Facet-based methodology has been used for ontology formalization for quite some time. Ontology formalization involves different steps such as, Identification of the terminology, Analysis, Synthesis, Standardization and Ordering. Firstly, for purposes of conceptualization OntoUML has been used which is a well-founded and established language for Ontology driven Conceptual Modelling. Phase transformation of "the same mode" has been subsequently obtained by OWL-DL using Protégé software. The final OWL ontology contains a total of around 232 axioms. These axioms comprise 148 logical axioms, 76 declaration axioms and 43 classes.

Deep Neural Networks (DNNs), from AlexNet to ResNet to ChatGPT, have made revolutionary progress in recent years, and are widely used in various fields. The high performance of DNNs requires a huge amount of high-quality data, expensive computing hardware, and excellent DNN architectures that are costly to obtain. Therefore, trained DNNs are becoming valuable assets and must be considered the Intellectual Property (IP) of the legitimate owner who created them, in order to protect trained DNN models from illegal reproduction, stealing, redistribution, or abuse. Although being a new emerging and interdisciplinary field, numerous DNN model IP protection methods have been proposed. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of two mainstream DNN IP protection methods: deep watermarking and deep fingerprinting, with a proposed taxonomy. More than 190 research contributions are included in this survey, covering many aspects of Deep IP Protection: problem definition, main threats and challenges, merits and demerits of deep watermarking and deep fingerprinting methods, evaluation metrics, and performance discussion. We finish the survey by identifying promising directions for future research.

We learn a visual representation that captures information about the camera that recorded a given photo. To do this, we train a multimodal embedding between image patches and the EXIF metadata that cameras automatically insert into image files. Our model represents this metadata by simply converting it to text and then processing it with a transformer. The features that we learn significantly outperform other self-supervised and supervised features on downstream image forensics and calibration tasks. In particular, we successfully localize spliced image regions "zero shot" by clustering the visual embeddings for all of the patches within an image.

Heritage Foundation senior fellow Michael Pillsbury weighs in on U.S.-China relations and Secretary of State Antony Blinken's trip to Beijing on'Fox News Tonight.' China has revealed plans to develop a futuristic warship that would include breakthrough technologies that would bring "science fiction to the real world," according to a report. "It will completely overturn the combat formation of naval fleets that has been in place for over a hundred years," People's Liberation Army (PLA) Navy Rear Adm. Ma Weiming said in a peer-reviewed paper, the South Morning China Post reported. The Chinese supership would look to adopt new technology such as rail guns, rocket launchers, laser weapons and high-powered microwaves. The technology would "cleverly and effectively transform the energy from the ship's power source – nuclear energy – into the electromagnetic energy needed to power" the weapons, Ma wrote.

The office of Texas State Attorney General Ken Paxton has requested that Pfizer and several other companies turn over advertising data tied to the social media giant Meta. The lawsuit was filed after consumer data privacy concerns were raised by the state in its latest legal battle with Meta, according to a report by Law360. The Texas Attorney General claims that millions of Texas residents have had their private biometric data misappropriated over the past ten years. The order requires the vaccine maker to share any records it holds regarding Meta's use of facial recognition technology over claims that the company was collecting biometric data from Facebook users without their consent. This decree over Pfizer's records follows a February 2022 filing against Meta by the Texas Attorney General that claimed "Facebook knowingly captured biometric information for its own commercial benefit" in order to "train and improve" its in-house facial recognition technology powered by AI.

A researcher was granted access earlier this year by Facebook's parent company, Meta, to incredibly potent artificial intelligence software – and leaked it to the world. As a former researcher on Meta's civic integrity and responsible AI teams, I am terrified by what could happen next. Though Meta was violated by the leak, it came out as the winner: researchers and independent coders are now racing to improve on or build on the back of LLaMA (Large Language Model Meta AI – Meta's branded version of a large language model or LLM, the type of software underlying ChatGPT), with many sharing their work openly with the world. This could position Meta as owner of the centrepiece of the dominant AI platform, much in the same way that Google controls the open-source Android operating system that is built on and adapted by device manufacturers globally. If Meta were to secure this central position in the AI ecosystem, it would have leverage to shape the direction of AI at a fundamental level, controlling both the experiences of individual users and setting limits on what other companies could and couldn't do.

Retired Brigadier General Robert Spalding joined'Fox & Friends Weekend' to discuss the significance of the report and broader concerns surrounding Chinese espionage targeting the U.S. Circa 1996, U.S. lawmakers wanted to make sure scrappy startups, like AOL and Amazon, had a fighting chance against incumbents. Our government had a straightforward approach: rubberstamp mergers and free tech from any regulatory oversight. These policy approaches were intended to level the playing field for the nascent tech industry and export our values abroad. Our tech policies of yore have turned those startups into the world's first set of trillion-dollar companies. But Big Tech failed to export our values -- and has even been counterproductive on that end.

Mike Gallagher and Raja Krishnamoorthi explain the threat from China amid growing concerns about TikTok and the country's relationship with Russia. Tech firm Ultra has developed an artificial intelligence-powered tool it believes has helped analysts identify products coming from China through the platform Temu that were created using forced labor, possibly from the Uyghur population. "We're looking at Temu from the perspective of the Forced Labor Prevention Act," Ultra founder and CEO Ram Ben Tzion told Fox News Digital. "How many things that we don't want are coming into the country using this method, right? The good cases are counterfeit. The worst cases are poor quality. "I'm quite confident that illicit elements can find themselves going through this platform into the market, so it's time to demand accountability," he added. Ben Tzion's company created the program Publican, which pulls in huge amounts of shipping data to analyze and look for patterns and red flags for any products ...

Learning a nonparametric system of ordinary differential equations (ODEs) from $n$ trajectory snapshots in a $d$-dimensional state space requires learning $d$ functions of $d$ variables. Explicit formulations scale quadratically in $d$ unless additional knowledge about system properties, such as sparsity and symmetries, is available. In this work, we propose a linear approach to learning using the implicit formulation provided by vector-valued Reproducing Kernel Hilbert Spaces. By rewriting the ODEs in a weaker integral form, which we subsequently minimize, we derive our learning algorithm. The minimization problem's solution for the vector field relies on multivariate occupation kernel functions associated with the solution trajectories. We validate our approach through experiments on highly nonlinear simulated and real data, where $d$ may exceed 100. We further demonstrate the versatility of the proposed method by learning a nonparametric first order quasilinear partial differential equation.