We introduce a new approach to agent programming, the development of LLM-based agents. Current approaches to agent programming often entangle two aspects of agent design: the core workflow logic and the inference-time strategy (e.g., tree search). We introduce "probabilistic angelic nondeterminism" ("PAN"), a programming model that disentangles these two concerns, allowing the programmer to describe the agent workflow and independently experiment with different inference-time strategies by simply changing a few inputs. We provide an implementation of PAN in Python as the EnCompass framework, which uses a Python decorator to compile agent workflow programs into a search space. We present three case studies that demonstrate how the framework lets the programmer quickly improve the reliability of an agent and easily switch between different inference-time strategies, all with little additional coding.

SURROUND Australia Pty Ltd demonstrates innovative applica-tions of the PROV Data Model (PROV-DM) and its Semantic Web variant, PROV-O, to systematically record and manage provenance information across multiple data processing domains. By employing RDF and Knowledge Graphs, SURROUND ad-dresses the critical challenges of shared entity identification and provenance granularity. The paper highlights the company's architecture for capturing comprehensive provenance data, en-abling robust validation, traceability, and knowledge inference. Through the examination of two projects, we illustrate how provenance mechanisms not only improve data reliability but also facilitate seamless integration across heterogeneous systems. Our findings underscore the importance of sophisticated provenance solutions in maintaining data integrity, serving as a reference for industry peers and academics engaged in provenance research and implementation. I. INTRODUCTION Encompass Australia Pty Ltd ("Encompass") is a little however unique innovation organization that has some expertise in giving state of the art simulated intelligence and information the executives items to both government and confidential area markets. Established with the mission to change how associations make due, cycle, and influence information, Encompass has quickly secured itself as a forerunner in the field by offering special and high level arrangements. At the center of Encompass' contributions lies its refined utilization of Semantic Web information, an innovative methodology that separates the organization from its rivals. Encompass solidly accepts that the Semantic Web is the best method for safeguarding significance after some time, empowering frameworks and hierarchical changes without the deficiency of basic setting.

This work aims to provide an overview on the open-source multilanguage tool called StyloMetrix. It offers stylometric text representations that cover various aspects of grammar, syntax and lexicon. StyloMetrix covers four languages: Polish as the primary language, English, Ukrainian and Russian. The normalized output of each feature can become a fruitful course for machine learning models and a valuable addition to the embeddings layer for any deep learning algorithm. We strive to provide a concise, but exhaustive overview on the application of the StyloMetrix vectors as well as explain the sets of the developed linguistic features. The experiments have shown promising results in supervised content classification with simple algorithms as Random Forest Classifier, Voting Classifier, Logistic Regression and others. The deep learning assessments have unveiled the usefulness of the StyloMetrix vectors at enhancing an embedding layer extracted from Transformer architectures. The StyloMetrix has proven itself to be a formidable source for the machine learning and deep learning algorithms to execute different classification tasks.

It is now 13 years since Harry Parker stepped on an improvised explosive device in Afghanistan, creating a blast that would result in the loss of both legs. Alongside the physical pain of the subsequent weeks, months and years, he also had to cope with a profound change in his sense of self. He compares the experience to that of Gregor Samsa, the subject of Franz Kafka's The Metamorphosis – "the strangeness of not being who you used to be, turned into something that sets you apart from those around you". Equipped with two hi-tech prosthetic limbs, Parker can now walk holding hands with his wife and carry his children on his shoulders. From the outside, it would be easy to conclude that he has adapted extraordinarily well to the event – and he says that "being an amputee feels normal".

Encompass Corporation, the provider of intelligently automated Know Your Customer (KYC) solutions, today announces its expansion into North America, with office headquarters based in New York. The expansion will allow Encompass to better serve the needs of existing global clients with a presence in North America, as well as secure new clients and partnerships in the region, in a marked effort to become the undisputed lead platform for automated, corporate KYC due diligence worldwide. Alex Ford has been appointed President, North America, overseeing all aspects of the GTM, driving business growth and working with customers, partners and the Encompass team to transform KYC with automation in financial institutions and other regulated entities. Encompass has received various notable awards recognition, including'Best Solution' category for Customer Onboarding - Regulation Asia Awards for Excellence, shortlisted in the British Bank Awards, winner of Red Herring's Top 100 Europe Award and regional winner at this year's Barclays Scale Up Entrepreneur of the Year. Joining in 2012, Alex has held Executive responsibility for several business functions at Encompass Corporation including Customer Success, Operations, Marketing, Product and Delivery.

Although the capabilities of Artificial Intelligence vastly outweigh human capacity in rapidly analysing data, it is crucial to understand the limitations. The primary advantage of AI encompasses the proficiency in outputting patterns and decisions, gathered through the quick analysis of information. Contrary to this, traditional decision making by humans encompass a more experience orientated approach, taking the alignment of morals and ethics into consideration. By approaching this through numerous lenses, and considering the different stages of machine learning; Artificial Intelligence, machine & deep learning, we can understand the position & potential of AI in corporate governance. Exposing a unique risk, the basis for AI learning stems from man-made data, and can experience a sense of bias and human error in early stages.

We live in a time where technology is advancing at a breakneck pace. Computing power has been rising at an exponential rate, allowing us to use it forevermore sophisticated tasks. Data analytics, Data Science and Machine Learning are three disciplines that have evolved in tandem with this tremendous expansion. But what is the difference between these 3 technologies that are so tightly linked? Let's find that out in the sections below.

The interactions between the Sun, interplanetary space, near Earth space environment, the Earth's surface, and the power grid are, perhaps unsurprisingly, very complicated. The study of such requires the collaboration between many different organizations spanning the public and private sectors. Thus, an important component of studying space weather is the integration and analysis of heterogeneous information. As such, we have developed a modular ontology to drive the core of the data integration and serve the needs of a highly interdisciplinary community. This paper presents our preliminary modular ontology, for space weather research, as well as demonstrate a method for adaptation to a particular use-case, through the use of existential rules and explicit typing.

One of the primary problems with artificial intelligence (AI) is the "artificial" part. The other is the "intelligence." While we like to pretend that we're setting robotic intelligences free from our human biases and other shortcomings, in reality we often transfer our failings into the AI, one dataset at a time. Hannah Davis, a data scientist, calls this out, arguing that "a dataset is a worldview," filled with subjective meanings. But rather than leave our AI hopes moribund, she also offers some ways we might improve the data that informs our AI.

The AI service will help Encompass customers to "quickly and accurately" find risk-relevant information on their own customers, organisations and investments. Sturgeon said: "Encompass is one of a number of international companies that has chosen to locate and steadily expand its operation, making Scotland an attractive place to grow its business. "From its Glasgow base, the company has access to markets, a supportive business environment and has been able to identify local talent from Scottish professionals in the engineering and software development sector. "Backed by almost £2 million of R&D investment from Scottish Enterprise, Encompass will be able to develop artificial intelligence software tools that will assist companies in the financial sector to reduce operational risks associated with meeting compliance and regulatory standards." Encompass's work in Glasgow has primarily been focused on its Know Your Customer product, where it uses data analytics to ensure clients do not unknowingly trade with organised crime or the proceeds of crime.