Abstract This paper addresses the significant challenge of recognizing behaviors in non-human primates, specifically focusing on chimpanzees. Automated behavior recognition is crucial for both conservation efforts and the advancement of behavioral research. However, it is often hindered by the labor-intensive process of manual video annotation. Despite the availability of large-scale animal behavior datasets, effectively applying machine learning models across varied environmental settings remains a critical challenge due to the variability in data collection contexts and the specificity of annotations. In this paper, we introduce ChimpBehave , a novel dataset comprising over 2 h and 20 min of video (approximately 215,000 frames) of zoo-housed chimpanzees, annotated with bounding boxes and fine-grained locomotive behavior labels. Uniquely, ChimpBehave aligns its behavior classes with those in PanAf, an existing dataset collected in distinct visual environments, enabling the study of cross-dataset generalization - where models are trained on one dataset and tested on another with differing data distributions. We benchmark ChimpBehave using state-of-the-art video-based and skeleton-based action recognition models, establishing performance baselines for both within-dataset and cross-dataset evaluations. Our results highlight the strengths and limitations of different model architectures, providing insights into the application of automated behavior recognition across diverse visual settings. The dataset, models, and code can be accessed at: https://github.com/MitchFuchs/ChimpBehave

Sensor networks are a primary source of massive amounts of data about the real world that surrounds us, measuring a wide range of physical parameters in real time. Given the hardware limitations and physical environment in which the sensors must operate, along with frequent changes of network topology, algorithms and protocols must be designed to provide a robust and energy efficient communications mechanism. With a view to addressing these constraints, this paper proposes a routing technique that is based on density based spatial clustering of applications with noise (DBSCAN) algorithm. This technique reveals several network topology semantics, enables the splitting of sensors responsibilities (communication/routing and sensing/monitoring), reduces the level of energy wasted on sending messages through the network by data aggregation only in cluster-head nodes and last but not the least, brings along very good results prolonging the network lifetime.

Grouping crimes having similarities has always been interesting for analysts. Actually, when a set of crimes share common properties, the capability to conduct reasoning and the automation with this set drastically increase. Conjunction, interpretation and explanation based on similarities can be key success factors to apprehend criminals. In this paper, we present a computerized method for high-volume crime linkage, based on a fuzzy MCDM approach in order to combine situational, behavioral, and forensic information. Experiments are conducted with series in burglaries from real data and compared to expert results.

This article tries to give an answer to a fundamental question intemporal data mining: "Under what conditions a temporal rule extracted from up-to-date temporal data keeps its confidence/support for future data". A possible solution is given by using, on the one hand, a temporal logic formalism which allows the definition of the main notions (event, temporal rule, support, confidence) in a formal way and, on the other hand, the stochastic limit theory. Under this probabilistic temporal framework, the equivalence between the existence of the support of a temporal rule and the law of large numbers is systematically analyzed.

In a database where numeric data has been obtained by inaccurate methods (measurements, calculations involving error propagation) it is unlikely to have equal stored values for a single, repeatedly measured/calculated value. As a result, instances which should satisfy but actually "nearly" satisfy a functional dependency cannot be decomposed. For such cases, we introduce the notion of statistical dependency (sd) as an extension of functional dependencies (fds). We show how the well-known axioms for fds can be used in the case of sds; decompositions w.r.t. sds are also presented. The resulting decomposition can be used to answer statistical queries. Finally, we study a possible way of generalizing the multivalued dependencies in the same manner.