Ukraine has branded Russia's presidency of the UN Security Council for the month of April "a symbolic blow," joining a chorus of outrage from Western countries. Moscow assumes the presidency as part of its monthly rotation between the Security Council's 15 member states, with ties with the West at their lowest point since the Cold War over Russia's invasion of Ukraine. Andriy Yermak, the Ukrainian president's chief of staff, said Russia's tenure was a "symbolic blow." It is another symbolic blow to the rules-based system of international relations," he wrote on Twitter. Ukraine's Foreign Minister Dmytro Kuleba said Russia assuming the presidency was "a slap in the face to the international community". "I urge the current UNSC members to thwart any Russian attempts to abuse its presidency," he wrote on Twitter on Saturday, calling Russia "an outlaw on the UNSC". Moscow last chaired the council in February 2022, the same month it invaded Ukraine – prompting Kyiv to call for Russia's removal from the council. Russia will hold little influence on decisions but will be in charge of the agenda. Moscow has said Foreign Minister Sergey Lavrov is planning to chair a UN Security Council meeting this month on "effective multilateralism". Russian foreign ministry spokeswoman Maria Zakharova also said that Lavrov would lead a debate on the Middle East on April 25. The Kremlin said on Friday it planned to "exercise all its rights" in the role. The White House urged Russia to "conduct itself professionally" when it assumes the role, saying there was no means to block Moscow from the post. "A country that flagrantly violates the UN Charter and invades its neighbour has no place on the UN Security Council," White House spokesperson Karine Jean-Pierre said on Friday. "Unfortunately, Russia is a permanent member of the Security Council and no feasible international legal pathway exists to change that reality," she added, calling the presidency "a largely ceremonial position". The Baltic states also expressed their concern. Estonia's UN envoy Rein Tammsaar, speaking also on behalf of Latvia and Lithuania, warned the Security Council Friday as it met to discuss Russia's plans to deploy tactical nuclear weapons in neighbouring Belarus. "Isn't it telling that tomorrow, on the anniversary of the Bucha killings, Russia will assume the Presidency of the UN Security Council?

Temporal and numerical expression understanding is of great importance in many downstream Natural Language Processing (NLP) and Information Retrieval (IR) tasks. However, much previous work covers only a few sub-types and focuses only on entity extraction, which severely limits the usability of identified mentions. In order for such entities to be useful in downstream scenarios, coverage and granularity of sub-types are important; and, even more so, providing resolution into concrete values that can be manipulated. Furthermore, most previous work addresses only a handful of languages. Here we describe a multi-lingual evaluation dataset - NTX - covering diverse temporal and numerical expressions across 14 languages and covering extraction, normalization, and resolution. Along with the dataset we provide a robust rule-based system as a strong baseline for comparisons against other models to be evaluated in this dataset. Data and code are available at \url{https://aka.ms/NTX}.

Writing a scientific article is a challenging task as it is a highly codified genre. Good writing skills are essential to properly convey ideas and results of research work. Since the majority of scientific articles are currently written in English, this exercise is all the more difficult for non-native English speakers as they additionally have to face language issues. This article aims to provide an overview of text revision in writing assistance in the scientific domain. We will examine the specificities of scientific writing, including the format and conventions commonly used in research articles. Additionally, this overview will explore the various types of writing assistance tools available for text revision. Despite the evolution of the technology behind these tools through the years, from rule-based approaches to deep neural-based ones, challenges still exist (tools' accessibility, limited consideration of the context, inexplicit use of discursive information, etc.)

Driver process models play a central role in the testing, verification, and development of automated and autonomous vehicle technologies. Prior models developed from control theory and physics-based rules are limited in automated vehicle applications due to their restricted behavioral repertoire. Data-driven machine learning models are more capable than rule-based models but are limited by the need for large training datasets and their lack of interpretability, i.e., an understandable link between input data and output behaviors. We propose a novel car following modeling approach using active inference, which has comparable behavioral flexibility to data-driven models while maintaining interpretability. We assessed the proposed model, the Active Inference Driving Agent (AIDA), through a benchmark analysis against the rule-based Intelligent Driver Model, and two neural network Behavior Cloning models. The models were trained and tested on a real-world driving dataset using a consistent process. The testing results showed that the AIDA predicted driving controls significantly better than the rule-based Intelligent Driver Model and had similar accuracy to the data-driven neural network models in three out of four evaluations. Subsequent interpretability analyses illustrated that the AIDA's learned distributions were consistent with driver behavior theory and that visualizations of the distributions could be used to directly comprehend the model's decision making process and correct model errors attributable to limited training data. The results indicate that the AIDA is a promising alternative to black-box data-driven models and suggest a need for further research focused on modeling driving style and model training with more diverse datasets.

Since its inception in the 1950s, artificial intelligence (AI) has made significant advancements. Because of improvements in processing power, machine learning methods, and the accessibility of massive data, the discipline has developed quickly. This essay will examine the development of AI and how it has altered our environment. Rule-based systems, where computers were programmed to make judgments based on a set of established rules, were the main focus of AI research in its early years. The only problems these systems could resolve were those that were specifically put into them, limiting their possibilities.

The problem of document structure reconstruction refers to converting digital or scanned documents into corresponding semantic structures. Most existing works mainly focus on splitting the boundary of each element in a single document page, neglecting the reconstruction of semantic structure in multi-page documents. This paper introduces hierarchical reconstruction of document structures as a novel task suitable for NLP and CV fields. To better evaluate the system performance on the new task, we built a large-scale dataset named HRDoc, which consists of 2,500 multi-page documents with nearly 2 million semantic units. Every document in HRDoc (a) Multi-page documents (b) Line-level classification has line-level annotations including categories and relations obtained from rule-based extractors and human annotators. Moreover, we proposed an encoder-decoder-based hierarchical document structure parsing system (DSPS) to tackle this problem. By adopting a multi-modal bidirectional encoder and a structure-aware GRU decoder with soft-mask operation, the DSPS model surpass the baseline method by a large margin.

Knowledge Graph Embeddings (KGE) have become a quite popular class of models specifically devised to deal with ontologies and graph structure data, as they can implicitly encode statistical dependencies between entities and relations in a latent space. KGE techniques are particularly effective for the biomedical domain, where it is quite common to deal with large knowledge graphs underlying complex interactions between biological and chemical objects. Recently in the literature, the PharmKG dataset has been proposed as one of the most challenging knowledge graph biomedical benchmark, with hundreds of thousands of relational facts between genes, diseases and chemicals. Despite KGEs can scale to very large relational domains, they generally fail at representing more complex relational dependencies between facts, like logic rules, which may be fundamental in complex experimental settings. In this paper, we exploit logic rules to enhance the embedding representations of KGEs on the PharmKG dataset. To this end, we adopt Relational Reasoning Network (R2N), a recently proposed neural-symbolic approach showing promising results on knowledge graph completion tasks. An R2N uses the available logic rules to build a neural architecture that reasons over KGE latent representations. In the experiments, we show that our approach is able to significantly improve the current state-of-the-art on the PharmKG dataset. Finally, we provide an ablation study to experimentally compare the effect of alternative sets of rules according to different selection criteria and varying the number of considered rules.

Detecting sets of relevant patterns from a given dataset is an important challenge in data mining. The relevance of a pattern, also called utility in the literature, is a subjective measure and can be actually assessed from very different points of view. Rule-based languages like Answer Set Programming (ASP) seem well suited for specifying user-provided criteria to assess pattern utility in a form of constraints; moreover, declarativity of ASP allows for a very easy switch between several criteria in order to analyze the dataset from different points of view. In this paper, we make steps toward extending the notion of High Utility Pattern Mining (HUPM); in particular we introduce a new framework that allows for new classes of utility criteria not considered in the previous literature. We also show how recent extensions of ASP with external functions can support a fast and effective encoding and testing of the new framework. To demonstrate the potential of the proposed framework, we exploit it as a building block for the definition of an innovative method for predicting ICU admission for COVID-19 patients. Finally, an extensive experimental activity demonstrates both from a quantitative and a qualitative point of view the effectiveness of the proposed approach.

With the continuous growth in communication network complexity and traffic volume, communication load balancing solutions are receiving increasing attention. Specifically, reinforcement learning (RL)-based methods have shown impressive performance compared with traditional rule-based methods. However, standard RL methods generally require an enormous amount of data to train, and generalize poorly to scenarios that are not encountered during training. We propose a policy reuse framework in which a policy selector chooses the most suitable pre-trained RL policy to execute based on the current traffic condition. Our method hinges on a policy bank composed of policies trained on a diverse set of traffic scenarios. When deploying to an unknown traffic scenario, we select a policy from the policy bank based on the similarity between the previous-day traffic of the current scenario and the traffic observed during training. Experiments demonstrate that this framework can outperform classical and adaptive rule-based methods by a large margin.

Banks and fraudsters are engaged in a never-ending game of cat and mouse. On one side, fraudsters move money around to remove traces of criminality. On the other, banks are on the lookout for suspicious activity that indicates financial fraud. "Criminals put money through the financial system in a series of layers to mask its original source, getting to a point where that money is cleaned and can be used and integrated into the financial system for any kind of purchase or investment," says Livia Benisty, chief business officer and former global head of AML at, Banking Circle – a payments bank that is pioneering the use of AI in AML. Money laundering regulations require banks and financial services to demonstrate methods for spotting this behaviour.