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Poisson Factorization (PF) is the gold standard framework for recommendation systems with implicit feedback whose variants show state-of-the-art performance on real-world recommendation tasks. However, most of the previous work do not explicitly take into account the temporal behavior of users which is essential to recommend the right item to the right user at the right time. In this paper, we introduce a Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback. RPF treats time as a natural constituent of the model, and takes important factors for recommendation into consideration to provide a... 

The interferometer method as one of the most accurate schemes for wideband direction finding (DF) is used. The interferometer method has various algorithms which can be implemented depending on the required specifications. The advantages and disadvantages of these algorithms have been evaluated and the appropriate algorithm for a general practical case in view of the ambiguity resolution is proposed. The receivers’ channel phase tracking error is of significant concern in practice in interferometric DF systems. The induced error due to channels phase tracking error is estimated. Furthermore the use of physically realizable antennas, achievement of high accuracy, minimum number of antennas... 

User modeling plays an important role in delivering customized web services to the users and improving their engagement. However, most user models in the literature do not explicitly consider the temporal behavior of users. More recently, continuous-time user modeling has gained considerable attention and many user behavior models have been proposed based on temporal point processes. However, typical point process-based models often considered the impact of peer influence and content on the user participation and neglected other factors. Gamification elements are among those factors that are neglected, while they have a strong impact on user participation in online services. In this article,... 

This paper introduces a novel framework for modeling temporal events with complex longitudinal dependency that are generated by dependent sources. This framework takes advantage of multidimensional point processes for modeling time of events. The intensity function of the proposed process is a mixture of intensities, and its complexity grows with the complexity of temporal patterns of data. Moreover, it utilizes a hierarchical dependent nonparametric approach to model marks of events. These capabilities allow the proposed model to adapt its temporal and topical complexity according to the complexity of data, which makes it a suitable candidate for real world scenarios. An online inference... 

The Markov decision process is one of the most common probabilistic prediction models used in infrastructure management. When existing data are insufficient, expert knowledge is commonly used to derive a Markovian transition probability matrix. Eventually, every pavement management system will progress to a level at which inspection measurements from the network will be organized into a database to be used for performance prediction. The best way to use this body of data to improve the initially developed transition probability matrix is to combine prior expert knowledge with new observations. This paper proposes a method for periodically updating Markovian transition probabilities as new... 

This paper presents a new inference algorithm for Active Learning Method (ALM). ALM is a pattern-based algorithm for soft computing which uses the Ink Drop Spread (IDS) algorithm as its main engine for feature extraction. In this paper a fuzzy number is extracted from each IDS plane rather than the narrow path and spread as in previous approaches. In order to compare performance of the proposed algorithm with the original one, two functions which are widely used in literature are modeled as the benchmark. Simulation results show that the proposed algorithm is as effective as previous one in the modeling task  

In this letter, a new adaptive Gaussian process (GP) frame work for signal extrapolation is proposed. Signal extrapolation is an essential task in many applications such as concealment of corrupted data in image and video communications. While possessing many interesting properties, Gaussian process priors with inappropriate stationary kernels may create extremely blurred edges in concealed areas of the image. To address this problem, we propose adaptive non-stationary kernels in a Gaussian process framework. The proposed adaptive kernel functions are defined based on the hypothesized edges of the missing areas. Experimental results verify the effectiveness of the proposed method compared to... 

There has been a strong push recently to examine biological scale simulations of neuromorphic algorithms to achieve stronger inference capabilities. This paper presents a set of piecewise linear spiking neuron models, which can reproduce different behaviors, similar to the biological neuron, both for a single neuron as well as a network of neurons. The proposed models are investigated, in terms of digital implementation feasibility and costs, targeting large scale hardware implementation. Hardware synthesis and physical implementations on FPGA show that the proposed models can produce precise neural behaviors with higher performance and considerably lower implementation costs compared with... 

An access control model for Semantic Web should take the semantic relationships among the entities, defined in the abstract conceptual level (i.e., ontology level), into account. Authorization and policy specification based on a logical model let us infer implicit security policies from the explicit ones based on the defined semantic relationships in the domains of subjects, objects, and actions. In this paper, we propose a logic based access control model for specification and inference of history-constrained access policies in conceptual level of Semantic Web. The proposed model (named TDLBAC-2) enables authorities to state policy rules based on the history of users' accesses using a... 

Ambient Intelligence is the future of computing where devices predict what users need and help them carry out their everyday life activities easier. To make this prediction possible these environments should be aware of the context. Activity recognition is one of the most complex problems in context-aware environments. In this paper we propose a layered Dynamic Bayesian Network (DBN) to recognize activities in an oral presentation. The layered architecture gives us the opportunity to recognize complex activities using the classification results of sensory data in the first layer regardless of the physical environment. Our model is event-driven meaning the classification takes place only when... 

E-learning environments are being used more efficiently by the rapid growth in internet and multimedia technologies. Adaptive learning is a kind of learning environment which provides individual learning. It can customize the learning style according to the individual's personality and characteristics. Although there are a lot of e-learning systems having adaptive learning feature, they do not satisfy all adaptive learning aspects. This paper proposes a new method which tries to help learners find educational contents adapted to their personalities in an efficient manner. Our proposed method has four essential parts: 1) It finds out learner's features by Bayesian networks. 2) Then It tries... 

In this research, we employ Bayesian inference and stochastic dynamic programming approaches to select the binomial population with the largest probability of success from n independent Bernoulli populations based upon the sample information. To do this, we first define a probability measure called belief for the event of selecting the best population. Second, we explain the way to model the selection problem using Bayesian inference. Third, we clarify the model by which we improve the beliefs and prove that it converges to select the best population. In this iterative approach, we update the beliefs by taking new observations on the populations under study. This is performed using Bayesian... 

This study presents a new method called FCSMM (Fuzzy Customer Satisfaction Measurement Method) for measuring individual customer satisfaction using a fuzzy inference system. The main advantage of this method is its simplification in evaluation of Customer Satisfaction Index (CSI) based on simple linguistic statements collected from experienced people. In contrast with assumptions used in other methods such as linear regression principles and predefined criteria weights, the aforementioned statements form the FCSMM computational structure. Since the drivers of satisfaction and dissatisfaction and performance indexes can be simultaneously applied, concurrent direct and indirect customer... 

In this paper, we use both stochastic dynamic programming and Bayesian inference concepts to design an optimum-acceptance-sampling-plan policy in quality control environments. To determine the optimum policy, we employ a combination of costs and risk functions in the objective function. Unlike previous studies, accepting or rejecting a batch are directly included in the action space of the proposed dynamic programming model. Using the posterior probability of the batch being in state p (the probability of non-conforming products), first, we formulate the problem into a stochastic dynamic programming model. Then, we derive some properties for the optimal value of the objective function, which... 

By the immense growth of the Web-Based Social Networks (WBSNs), the role of trust in connecting people together through WBSNs is getting more important than ever. In other words, since the probability of malicious behavior in WBSNs is increasing, it is necessary to evaluate the reliability of a person before trying to communicate with. Hence, it is desirable to find out how much a person should trust another one in a network. The approach to answer this question is usually called trust inference. In this paper, we propose a new trust inference algorithm (Called RN-Trust) based on the resistive networks concept. The algorithm, in addition to being simple, resolves some problems of previously... 

A mobile ad hoc network is a collection of mobile hosts forming a temporary network on the fly, without using any fixed infrastructure. Characteristics of mobile ad hoc networks such as lack of central coordination, mobility of hosts, dynamically varying network topology, and limited availability of resources make QoS provisioning very challenging in such networks. In this paper, we introduce a fuzzy QoS traffic conditioner for mobile ad hoc networks. The proposed traffic conditioner consists of fuzzy admission control (FAC), fuzzy traffic rate controller (FTRC), and fuzzy scheduler (FS). The proposed FAC monitors the delay and available bandwidth and decides whether to accept or reject the... 

A new hybrid fuzzy adaptive algorithm for tracking maneuvering targets is proposed in this paper. The algorithm is implemented with fuzzy inference system (FIS) and current statistical model and adaptive Altering (CSMAF). The CSMAF algorithm is one of most effective methods for tracking the maneuvering targets. It has a higher precision in tracking the maneuvering targets with larger accelerations while it has a lower precision in tracking the maneuvering targets with smaller acceleration. In the proposed algorithm, to overcome the disadvantage of the CSMAF algorithm, the covariance of process noise CSMAF is adjusted adaptively by the output of a FIS. The input of the FIS is discrepancy of... 

In this paper, an adaptive critic-based neurofuzzy controller is presented for water level regulation of nuclear steam generators. The problem has been of great concern for many years as the steam generator is a highly nonlinear system showing inverse response dynamics especially at low operating power levels. Fuzzy critic-based learning is a reinforcement learning method based on dynamic programming. The only information available for the critic agent is the system feedback which is interpreted as the last action the controller has performed in the previous state. The signal produced by the critic agent is used alongside the backpropagation of error algorithm to tune online conclusion parts... 

One of the problems in Bayesian inference is the prior selection. We can categorize different methods for selecting prior into two main groups: informative and non-informative. Here, we have considered an informative method called filters random filed and minimax entropy (FRAME). Despite of its theoretical interest, that method introduces a huge amount of computational burden, which makes it very unsuitable for real-time applications. The main critical point of the method is its parameter estimation part, which plays a major role in its very low speed. In this paper, we have introduced a fast method for parameter estimation to fasten the FRAME approach. Although the kernel of our approach is... 

In order to design a decision-making framework in production environments, in this study, we use both the stochastic dynamic programming and Bayesian inference concepts. Using the posterior probability of the production process to be in state λ (the hazard rate of defective products), first we formulate the problem into a stochastic dynamic programming model. Next, we derive some properties for the optimal value of the objective function. Then, we propose a solution algorithm. At the end, the applications and the performances of the proposed methodology are demonstrated by two numerical examples. © 2007 Asian Network for Scientific Information