Theory-based user modeling for personalized interactive information retrieval

In an effort to improve users’ search experiences during their information seeking process, providing a personalized information retrieval system is proposed to be one of the effective approaches. To personalize the search systems requires a good understanding of the users. User modeling has been approved to be a good method for learning and representing users. Therefore many user modeling studies have been carried out and some user models have been developed. The majority of the user modeling studies applies inductive approach, and only small number of studies employs deductive approach. In this paper, an EISE (Extended Information goal, Search strategy and Evaluation threshold) user model is proposed, which uses the deductive approach based on psychology theories and an existing user model. Ten users’ interactive search log obtained from the real search engine is applied to validate the proposed user model. The preliminary validation results show that the EISE model can be applied to identify different types of users. The search preferences of the different user types can be applied to inform interactive search system design and development

A MODELING-BASED CLASSIFICATION ALGORITHM VALIDATED WITH SIMULATED DATA

We present a Generalized Lotka-Volterra (GLV) based approach for modeling and simulation of supervised inductive learning, and construction of an efficient classification algorithm. The GLV equations, typically used to explain the biological world, are employed to simulate the process of inductive learning. In addition, the modeling approach provides a key advantage over the more conventional constraint and optimization-based classification algorithms, as influences of outliers and local patterns, which can lead to problematic overfitting, are auto-moderated by the model itself. We present the bare-bones algorithm and motivate the model through axiomatic postulates. The algorithm is validated using benchmark simulated datasets, showing results competitive with other cutting-edge algorithms.

Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis

Even with impressive advances in automated formal methods, certain problems in system verification and synthesis remain challenging. Examples include the verification of quantitative properties of software involving constraints on timing and energy consumption, and the automatic synthesis of systems from specifications. The major challenges include environment modeling, incompleteness in specifications, and the complexity of underlying decision problems. This position paper proposes sciduction, an approach to tackle these challenges by integrating inductive inference, deductive reasoning, and structure hypotheses. Deductive reasoning, which leads from general rules or concepts to conclusions about specific problem instances, includes techniques such as logical inference and constraint solving. Inductive inference, which generalizes from specific instances to yield a concept, includes algorithmic learning from examples. Structure hypotheses are used to define the class of artifacts, such as invariants or program fragments, generated during verification or synthesis. Sciduction constrains inductive and deductive reasoning using structure hypotheses, and actively combines inductive and deductive reasoning: for instance, deductive techniques generate examples for learning, and inductive reasoning is used to guide the deductive engines. We illustrate this approach with three applications: (i) timing analysis of software; (ii) synthesis of loop-free programs, and (iii) controller synthesis for hybrid systems. Some future applications are also discussed

Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence

Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a "picture" not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining approaches described in the literature produce such models. This is in stark contrast with the over 25 available commercial process mining tools that only discover informal process models that remain deliberately vague on the precise set of possible traces. There are two main reasons why vendors resort to such models: scalability and simplicity. In this paper, we propose to combine the best of both worlds: discovering hybrid process models that have formal and informal elements. As a proof of concept we present a discovery technique based on hybrid Petri nets. These models allow for formal reasoning, but also reveal information that cannot be captured in mainstream formal models. A novel discovery algorithm returning hybrid Petri nets has been implemented in ProM and has been applied to several real-life event logs. The results clearly demonstrate the advantages of remaining "vague" when there is not enough "evidence" in the data or standard modeling constructs do not "fit". Moreover, the approach is scalable enough to be incorporated in industrial-strength process mining tools.Comment: 25 pages, 12 figure

Characterisation and macro-modeling of patterned micronic and nano-scale dummy metal-fills in integrated circuits

In this paper, a wideband characterization and macro-modeling of patterned micronic and nano-scale dummy metal-fills is presented. Impacts of patterned dummy metal-fill topologies including square, cross, vertical and horizontal shaped arrays on electrical performances (isolation/coupling, attenuation, guiding properties, etc…) are investigated. The validity of the proposed macro-modeling methodology is demonstrated by comparison with high frequency measurements of dedicated carrier structures including on-chip interconnects and RF inductive loops. An original extraction approach, based on local ground concept, is proposed to capture high frequency behaviour of dummy metal-fill in physics-based compact broadband SPICE model. The RLC parameters are accurately derived using fully scalable closed-form semi-analytical expressions

Non-Invasive Induction Link Model for Implantable Biomedical Microsystems: Pacemaker to Monitor Arrhythmic Patients in Body Area Networks

In this paper, a non-invasive inductive link model for an Implantable Biomedical Microsystems (IBMs) such as, a pacemaker to monitor Arrhythmic Patients (APs) in Body Area Networks (BANs) is proposed. The model acts as a driving source to keep the batteries charged, inside a device called, pacemaker. The device monitors any drift from natural human heart beats, a condition of arrythmia and also in turn, produces electrical pulses that create forced rhythms that, matches with the original normal heart rhythms. It constantly sends a medical report to the health center to keep the medical personnel aware of the patient's conditions and let them handle any critical condition, before it actually happens. Two equivalent models are compared by carrying the simulations, based on the parameters of voltage gain and link efficiency. Results depict that the series tuned primary and parallel tuned secondary circuit achieves the best results for both the parameters, keeping in view the constraint of coupling co-efficient (k), which should be less than a value \emph{0.45} as, desirable for the safety of body tissues.Comment: IEEE 8th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA'13), Compiegne, Franc

Modeling as Scientific Reasoning—The Role of Abductive Reasoning for Modeling Competence

While the hypothetico-deductive approach, which includes inductive and deductive reasoning, is largely recognized in scientific reasoning, there is not much focus on abductive reasoning. Abductive reasoning describes the theory-based attempt of explaining a phenomenon by a cause. By integrating abductive reasoning into a framework for modeling competence, we strengthen the idea of modeling being a key practice of science. The framework for modeling competence theoretically describes competence levels structuring the modeling process into model construction and model application. The aim of this theoretical paper is to extend the framework for modeling competence by including abductive reasoning, with impact on the whole modeling process. Abductive reasoning can be understood as knowledge expanding in the process of model construction. In combination with deductive reasoning in model application, such inferences might enrich modeling processes. Abductive reasoning to explain a phenomenon from the best fitting guess is important for model construction and may foster the deduction of hypotheses from the model and further testing them empirically. Recent studies and examples of learners’ performance in modeling processes support abductive reasoning being a part of modeling competence within scientific reasoning. The extended framework can be used for teaching and learning to foster scientific reasoning competences within modeling processes.Peer Reviewe

Blurring Diffusion Models

Recently, Rissanen et al., (2022) have presented a new type of diffusion process for generative modeling based on heat dissipation, or blurring, as an alternative to isotropic Gaussian diffusion. Here, we show that blurring can equivalently be defined through a Gaussian diffusion process with non-isotropic noise. In making this connection, we bridge the gap between inverse heat dissipation and denoising diffusion, and we shed light on the inductive bias that results from this modeling choice. Finally, we propose a generalized class of diffusion models that offers the best of both standard Gaussian denoising diffusion and inverse heat dissipation, which we call Blurring Diffusion Models