Chemical Semantics (United States)

In multimedia forensics, most research focuses on analyzing individual assets, such as images or videos, to verify their authenticity by analyzing their intrinsic properties. However, provenance analysis examines multiple assets collectively, tracing their histories through pairwise relationships. This approach is beneficial in identifying traces of manipulation and understanding the evolution of digital content. In this research, we apply provenance analysis to detect and track malicious users engaged in collusion attacks by analyzing their fingerprinting codes. Specifically, the correlations we consider are not generic media content similarities, but rather statistical and structural relationships between the embedded fingerprinting codes of different multimedia assets. By quantifying these correlations, our method can reliably identify individuals responsible for generating unauthorized copies and distinguish between different collusion strategies. Furthermore, it allows us to classify and estimate the specific type of collusion attack employed, providing deeper insights into the strategies used by attackers, thereby aiding in the development of more robust countermeasures and enhancing the resilience of multimedia security systems.

Random experiments that are simple and clear enough to be performed by human agents feature prominently in the teaching of elementary stochastics as well as in games. We present Alea, a domain-specific language for the specification of random experiments. Alea code can either be analyzed statically to obtain and inspect probability distributions of outcomes, or be executed with a source pseudo-randomness for simulation or as a game assistant. The language is intended for ease of use by non-expert programmers, in particular students of elementary stochastics, and players and designers of games of chance, by focusing on concepts common to functional programming and basic mathematics. Both the design of the language and the implementation of runtime environments are work in progress.

This study examines the use of ontological frameworks in wind energy, specifically their impact on improving the management and performance of wind turbines throughout their lifespan. This work offers a succinct examination of the underlying principles of ontology, their practical application in design and development, and real-life examples that showcase their influence in standardizing understanding and enhancing decision-making in wind energy sector. The text classifies and evaluates several wind energy ontologies, providing valuable perspectives for scholars and practitioners. Furthermore, it tackles obstacles and emphasizes possibilities for utilizing ontologies to stimulate advancement in wind turbine technology. This survey aims to be a guide for those interested in the intersection of ontologies and wind energy.

The present paper employs topological data analysis methods to reveal ‘holes’ (stable persistent homologies) in the semantic spaces of words, bigrams, and trigrams of the English and Russian languages, and to ascertain their boundaries. Furthermore, the paper selects those holes that belong to the large‐scale (coarse‐grained) structure of the language that are not just local inhomogeneities of the sample—it appears that there are around a dozen of them for each of the languages (English and Russian). These boundaries delineate ‘blind spots’ of the respective language—the regions of the semantic spaces that do not contain words/bigrams/trigrams of the language—that is, regions of concepts that the language cannot see through its lens. The secondary goal of the paper is to solve the bot‐detection problem in its strong statement, that is, one trains the classifiers on one set of bots and tests on the another set of bots. To this end, we estimate the average distances from words, bigrams, and trigrams of a text to the boundaries of the nearest ‘hole’, for texts both written by humans and generated by bots, and construct classifiers. The classifiers show comparatively good results: the average accuracy amounts to 0.8.

Abstract AimsT1 mapping on cardiac magnetic resonance (CMR) imaging is useful for diagnosis and prognostication in patients with light-chain cardiac amyloidosis (AL-CA). We conducted this study to evaluate the performance of T1 mapping parameters for detection of cardiac amyloidosis (CA) in patients with left ventricular hypertrophy (LVH) and their prognostic values in patients with AL-CA, using a semi-automated deep learning algorithm.Methods and ResultsA total of 300 patients who underwent CMR for differential diagnosis of LVH were analyzed. CA was confirmed in 50 patients (39 with AL-CA and 11 with transthyretin amyloidosis), hypertrophic cardiomyopathy in 198, hypertensive heart disease in 47, and Fabry disease in 5. A semi-automated deep learning algorithm (Myomics-Q) was used for the analysis of the CMR images. The optimal cutoff extracellular volume fraction (ECV) for the differentiation of CA from other etiologies was 33.6% (diagnostic accuracy 85.6%). he artificial intelligence (AI)-derived ECV showed a significant prognostic value for a composite of cardiovascular death and heart failure hospitalization in patients with AL-CA (revised Mayo stage III or IV) (adjusted hazard ratio 4.247 for ECV ≥40%, 95% confidence interval 1.215–14.851, p-value=0.024). Incorporation of AI-derived ECV into the revised Mayo staging system resulted in better risk stratification (integrated discrimination index 27.9%, p=0.013; net reclassification index 13.8%, p=0.007).ConclusionsAI-assisted T1 mapping on CMR imaging allows for improved diagnosis of CA from other etiologies of LVH. Furthermore, AI-derived ECV has significant prognostic value in patients with AL-CA, suggesting its clinical usefulness.Graphical Abstract

Сільське господарство вважається особливою сферою виробництва, оскільки в процесі створення готової про дукції поряд з фінансовими і матеріально технічними ресурсами задіяні також природні ресурси: земля і живі орган ізми. Введення в дію НПБО 30 "Біологічні активи", яким змінено організацію та методику обліку тварин у сільськогос подарських організаціях. Як відомо, нова методологія бухгалтерського обліку, яка була реалізована в Національно му стандарті бухгалтерського обліку 30 "Біологічні активи", призвела до численних проблем у бухгалтерській про фесії та не знайшла широкого впровадження в сільськогосподарських підприємствах країни. У статті зазначено, що існують численні проблеми щодо організації та управління біологічними активами (відсутність чіткого визначення, класифікації, неефективна система синтетичного та аналітичного обліку тощо). It has been determined that the population of Ukraine lacks meat and meat products. In solving the issue of increasing meat and livestock production, the approach of this industry is effective, first of all. The shortsightedness of agricultural managers, low liquidity and difficult financial situation led to the destruction of livestock farming in organizations that were private: partnerships and leased enterprises. Compared to the expected period of the administrative economy, the livestock industry is preserved in collective ownership organizations that are part of an agricultural production cooperative, which follows the introduction of market principles of management in the agricultural sector and the preservation of land and property, which allows for the preservation of the integrity of land and property complexes. It has been proven that livestock breeding is of great economic importance for the country as a whole. It is considered a source of providing the population with such important food products as meat, milk, eggs, and also provides the industry with wool, leather, fur and other resources. Agriculture is considered to be a special sphere of production, as in the process of creating finished products, along with financial, material and technical, and labor resources, natural resources are also involved: land and living organisms. Despite the importance of accounting policies, there is a problem in agricultural organizations with their creation; the managers of the organization specifically address this issue and do not take into account the specifics of the organization's activities when creating them.

Solving the wicked problems of food system sustainability requires a process of knowledge co-production among diverse actors in society. We illustrate a generalized workflow for knowledge co-production in food systems with a pair of case studies from the response of the meat and dairy production sectors in the wake of the COVID-19 pandemic. The first case study serves as an example of a scientific workflow and uses a GIS method (location allocation) to examine the supply chain linkages between meat and dairy producers and processors in Ohio. This analysis found that meat producers and processors are less clustered and more evenly distributed across the state than dairy producers and processors, with some dairy processors potentially needing to rely on supply from producers up to 252 km away. The second case study in California adds an example of a stakeholder workflow in parallel to a scientific workflow and describes the outcome of a series of interviews with small and mid-scale meat producers and processors concerning their challenges and opportunities, with the concentration of processors arising as the top challenge faced by producers. We present a pair of workflow diagrams for the two case studies that illustrate where the processes of knowledge co-production are situated. Examining these workflow processes highlights the importance of data privacy, data governance, and boundary spanners that connect stakeholders.

Ushbu maqolada o‘zlashmalar, ularga berilgan ta’riflar, nemis tiliga boshqa chet tillardan o‘zlashgan so‘zlar, o‘zlashish yo‘llari, so‘zlarning o‘zlashishga sabab bo‘luvchi omillar va nemis tiliga o‘zlashgan so‘zlarning strukturasi va semantikasi haqida so‘z boradi. Maqolaning kirish qismida o‘zlashma haqida umumiy ma’lumotlar keltiriladi. So‘ngra maqolada foydalanilgan adabiyotlar va metodologiyasi haqida so‘z boradi. Muhokama qismida asosiy muammolar haqida fikr yuritilsa, natijalar qismida nemis tiliga o‘zlashgan so‘zlarning struktural –semantik jihatdan tahlili berilgan. Maqolaning so‘ngida esa mavzu jihatda xulasalar berilgan.

Most work on verbalising Knowledge-Graphs (KG) has focused on high-resource languages such as English, Russian, Czech or Arabic. In this paper, we focus on KG-to-Text generation where the output text is in Breton, Irish or Welsh. To overcome the small size of the parallel training data, we combine the strengths of a multilingual encoder-decoder model with denoising fine-tuning on monolingual data and Soft Prompt fine-tuning on a small quantity of KG/text data. We furthermore structure the soft prompt into multiple sub-prompts designed to capture the similarities and differences between English, Knowledge graphs and the three target languages. Our experiments show that our approach outperforms strong baselines and that all sub-prompts contribute to performance 1 .

We propose to present a set of web audio plugins specialized in three kinds of sounds. The first one targets blues / classic rock sounds and proposes a drop down menu with presets that go from clean warm blues like the tones used by BB King, to more classic rock/blues distorted sounds as used by Jimmy Hendrix or AC/DC. The second one is aimed to Hi-Gain/Metal sounds similar to Mesa Boogie type of sounds, and the third one is an acoustic guitar simulator (enabling the use of an electric guitar to get an acoustic folk guitar sound). These plugins are partially available in open source, but the versions we propose to demo are commercialized by our laboratory (French CNRS/SATT Sud-Est) and included in some commercial DAWs.