Creating Input Sets For Inductive Learning From Simple Events

Event-driven Process Chain (EPC) is a known business process modeling language which is primarily renowned for efficient modeling of business requirements. It is a semi-formal modeling language which is commonly verified though various formal mechanism for simple atomic events. However, the complexity of business requirements cannot be modeled through EPC atomic events alone especially in case of real time systems. Therefore, the complex events patterns like Event Time, Event location, Event Cardinality, Event Sequence, Event Exclusion, Event Trend and Data dependency are introduced in EPC from the novel area of complex event processing. However, to the best of our knowledge, there is no mechanism available in EPC to model and verify such complex events. Therefore, in this paper, we investigate the verification possibilities of both (atomic and complex) events in the context of Timed-Automata (TA) and Time Petri Nets (TPN). Particularly, we try to map the semantics of simple and complex events with the corresponding concepts of TA and TPN. It is analyzed that the simple EPC events can be formalized through TA and TPN but limited support is available for complex events. The restaurant process case study has been used for further investigation of complex events. It has been concluded that TA formalism is more suitable than TPN for the verification of complex events in EPC.

We present a general framework for complex event recognition that is well-suited for integrating information that varies widely in detail and granularity. Consider the scenario of an agent in an instrumented space performing a complex task while describing what he is doing in a natural manner. The system takes in a variety of information, including objects and gestures recognized by RGB-D and descriptions of events extracted from recognized and parsed speech. The system outputs a complete reconstruction of the agent's plan, explaining actions in terms of more complex activities and filling in unobserved but necessary events. We show how to use Markov Logic (a probabilistic extension of first-order logic) to create a model in which observations can be partial, noisy, and refer to future or temporally ambiguous events; complex events are composed from simpler events in a manner that exposes their structure for inference and learning; and uncertainty is handled in a sound probabilistic manner. We demonstrate the effectiveness of the approach for tracking kitchen activities in the presence of noisy and incomplete observations.

In the context of computer vision, most of the traditional action recognition techniques assign a single label to a video after analyzing the whole video. We believe that under- standing of the visual world is not limited to recognizing a specific action class or individual object instances, but also extends to how those objects interact in the scene, which im- plies recognizing events happening in the scene. In this paper we present an approach for identifying complex events in videos, starting from detection of objects and simple events using a state-of-the-art object detector (YOLO). We provide a logic based representation of events by using a realization of the Event calculus that allows us to define complex events in terms of logical rules. Axioms of the calculus are encoded in a logic program under Answer Set semantics in order to reason and formulate queries over the extracted events. The applicability of the framework is demonstrated over the scenario of recognizing different kinds of kick events in soccer videos.

The notion of "events" is extremely important in characterizing the contents of video. An event is typically triggered by some kind of change of state captured in the video, such as when an object starts moving. The ability to reason with events is a critical step toward video understanding. This article describes the findings of a recent workshop series that has produced an ontology framework for representing video events-called Video Event Representation Language (VERL) -and a companion annotation framework, called Video Event Markup Language (VEML). One of the key concepts in this work is the modeling of events as composable, whereby complex events are constructed from simpler events by operations such as sequencing, iteration, and alternation. The article presents an extensible event and object ontology expressed in VERL and discusses a detailed example of applying VERL and VEML to the description of a "tailgating" event in surveillance video.

Composite event monitoring in XML repositories using generic rule framework for providing reactive e-services

Turbidity behavior in streams is a complex and dynamic function of both source material supply and event‐driven transport. While the primary controls on turbidity behavior across time and space are still not fully understood, recent increases in the availability of high temporal resolution, colocated stream turbidity, and discharge data provide an opportunity for more detailed analysis. Here we examine methods to quantitatively characterize event responses by modeling the shape of turbidity‐discharge hysteresis loops. A total of 1559 events from 20 gages in the Mid‐Atlantic region of the U.S. were modeled using both previously reported models and new models combining elements of existing models. The results suggest that a more general power law‐based model, utilizing both a discharge rate of change term and a “supply” term, allows characterization of a wide range of simple and complex events. Additionally, this study explores a decorrelation approach to address the strong correlation frequently observed between the power law model coefficient ( ) and exponent ( ), with the goal of exposing the underlying behavior of each parameter individually. An examination of seasonal parameter behavior suggests that this approach may facilitate greater physically based interpretation of the power law coefficient. The power law parameter decorrelation strategy and the loop models examined here provide a step toward the larger goal of understanding the physical controls on turbidity‐discharge hysteretic behavior.

Event correlation is an important component in business performance management. Simple rule-based event correlation engines, like Zurich Correlation Engine (ZCE), usually accept input events that include only name-value pairs. However, when input events are in XML format, the ZCE needs to be adapted to accept complex/structural events. In this paper, we present a model-driven approach to derive solutions for adapting the new type of events, so that the light weight correlation engine can still be used for event monitoring and correlation.

The Internet of Things (IoT) has grown rapidly to become the core of many areas of application, leading to the integration of sensors, with IoT devices. However, the number of attacks against these types of devices has grown as fast as the paradigm itself. Certain inherent characteristics of the paradigm, as well as the limited computational capabilities of the devices involved, make it difficult to deploy security measures. This is why it is necessary to design, implement and study new solutions in the field of cybersecurity. In this paper, we propose an architecture that is capable of generating Complex Event Processing (CEP) rules automatically by integrating them with machine learning technologies. While the former is used to automatically detect attack patterns in real time, the latter, through the use of the Principal Component Analysis (PCA) algorithm, allows the characterization of events and the recognition of anomalies. This combination makes it possible to achieve efficient CEP rules at the computational level, with the results showing that the CEP rules obtained using our approach substantially improve upon the performance of the standard CEP rules, which are rules that are not generated by our proposal but can be defined independently by an expert in the field. Our proposal has achieved an F1-score of 0.98 on average, a 76 percent improvement in throughput over standard CEP rules, and a reduction in the network overhead of 86 percent over standard simple events, which are the simple events that are generated when our proposal is not used.

The exploration of intracellular vesicles plays crucial roles for neuronal activity assessment. Neurons at different growth stages may possess distinct neuronal activity, including vesicular content and release kinetics. Here, we monitored the vesicular content and its release kinetics in neurons at different growth stages by intracellular vesicle electrochemical cytometry. We found that the neurotransmitters content of vesicles changed to be increased and the vesicle release process became longer as the neurons grew. Further, we demonstrated that the vesicular adsorption and rupture modes changed from the dominant simple event to simple and complex event coexisting mode. We speculate that this work provides a new strategy for the neuronal activity assessment or real‐ time cell activity analysis.

Is stillbirth associated with long-term atherosclerotic morbidity?

People often have knowledge about the chances of events but are unable to express their knowledge in the form of coherent probabilities. This study proposed to correct incoherent judgment via an optimization procedure that seeks the (coherent) probability distribution nearest to a judge's estimates of chance. This method was applied to the chances of simple and complex meteorological events, as estimated by college undergraduates. No judge responded coherently, but the optimization method found close (coherent) approximations to their estimates. Moreover, the approximations were reliably more accurate than the original estimates, as measured by the quadratic scoring rule. Methods for correcting incoherence facilitate the analysis of expected utility and allow human judgment to be more easily exploited in the construction of expert systems.

This paper investigates the distribution of reflexive and nonreflexive pronouns in the prepositional phrase, concluding that multiple semantic factors play a role in the appearance of one pronoun over the other. The distributional trends in English are explained by referencing the crucial role space plays in grammar, and the resulting implications for Binding Theory (Chomsky 1995) are discussed. The motivating forces for the corpus distribution are based on perceived directionality and location of the denoted event with respect to the body of the event's protagonist. The patterns found in the corpus data are attributed to a range of factors that play a role in the semantic specifications and associations of the pronouns themselves. First, it is argued that the high rate of reflexive pronouns in events that are metaphorically located in the body is due to the reflexive pronoun's close semantic association with the concept of self, a metaphorical body-internal entity. Second, it is argued that the reflexive pronoun is used to signal either an event which is performed on the body (in the referent's peri-personal space) or directed toward the body. Cases of these types are explained by a schematic, semantic parallelism between syntactically complex reflexive events and syntactically simple reflexive events. In both cases, the reflexive pronoun signals a contrastive element. In syntactically complex cases, the PP examples (e.g. John pushed the box toward himself), and syntactically simple cases, those with basic clause structure (e.g. John kicked himself), the reflexive is used to signal that the direction of the event is counter to the direction of expectation, thus explaining why certain reflexive events (e.g. bathe, or pull something toward you) do not have to, and most often, do not occur with the reflexive pronoun.

This study investigates adaptation of high-frequency cortical responses [>60 Hz; high-gamma (HG)] to simple and complex sounds in human nonprimary auditory cortex. We used intracranial electrocorticographic recordings to measure event-related changes in HG power as a function of stimulus probability. Tone and speech stimuli were presented in a series of traditional oddball and control paradigms. We hypothesized that HG power attenuates with stimulus repetition over multiple concurrent time scales in auditory association cortex. Time-frequency analyses were performed to identify auditory-responsive sites. Single-trial analyses and quantitative modeling were then used to measure trial-to-trial changes in HG power for high (frequent), low (infrequent), and equal (control) stimulus probabilities. Results show strong reduction of HG responses to frequently repeated tones and speech, with no differences in responses to infrequent and equal-probability stimuli. Adaptation of the HG frequent response, and not stimulus-acoustic differences or deviance-detection enhancement effects, accounted for the differential responses observed for frequent and infrequent sounds. Adaptation of HG responses showed a rapid onset (less than two trials) with slower adaptation between consecutive, repeated trials (2-10 s) and across trials in a stimulus block (∼7 min). The auditory-evoked N100 response also showed repetition-related adaptation, consistent with previous human scalp and animal single-unit recordings. These findings indicate that HG responses are highly sensitive to the regularities of simple and complex auditory events and show adaptation on multiple concurrent time scales in human auditory association cortex.

One experiment compared the effect of elaboration on enacted and non-enacted events. The commands were either presented in a basic form (e.g., "wave your hands") or in an enriched form. The commands were enriched by adding statements to the commands of how to perform the actions (e.g., "wave your hands as a conductor"). Free- and cued-recall data showed elaboration to have a dissociative effect on enacted and non-enacted events. Memory for the non-enacted events benefited from enrichment, whereas simple enacted events were remembered to a higher extent than complex enacted events. Lack of benefit from elaboration on memory of enacted events is suggested to be due to enactment leading to a sufficient degree of item-specific processing, and a negative effect of elaboration is suggested to occur when the way of manipulating item complexity decreases the familiarity of the actions. Familiarity ratings of the items by two independent groups of subjects supported this interpretation.

The advent of Single Instruction Multiple Data (SIMD) instructions in modern processors has revolutionized data processing by enabling simultaneous computation across multiple data elements. While database systems have extensively adopted SIMD for traditional operations, its potential for complex event pattern matching remains largely unexplored. This paper presents a novel approach that bridges this gap through bit-parallel processing enhanced with AVX-512 vectorization. Our approach encodes event streams into compact bit sequences, where each bit corresponds to a time slice, and an event's presence is marked by a 1-bit when its timestamp falls within the respective slice. This representation enables the formulation of bit-parallel operations that natively enforce complex event constraints, including temporal window requirements and event ordering relationships. We develop a family of bit-parallel algorithms that leverage this representation for continuous event matching, and further optimize their performance through SIMD vectorization (AVX-512 instructions) to exploit modern hardware parallelism. Experimental evaluations on both real-world and synthetic datasets demonstrate the superiority of our method, achieving at least 35.7x improvement in query efficiency compared to state-of-the-art alternatives.