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Recommender systems are ubiquitous in applications ranging from e-commerce to social media, helping users to navigate a huge selection of items and to meet a variety of special needs and user tastes. Incorporating contextual knowledge into such systems — such as relational information — has proven to be an effective way to improve recommendation accuracy. A popular line of research aims to model relationships...
When dealing with Quality of Experience (QoE) and in particular perceptual quality assessment and modeling, averaging is a common occurrence. For instance, the most commonly used measure of QoE is the aptly-called Mean Opinion Score (MOS), which is intended to represent an idealized average subject's rating of the quality. Another form of averaging occurs when choosing and preparing the samples used...
In this paper, we propose a method for improving the maneuverability of master-slave systems. We aim at reproducing human skillfulness and dynamic performance in master-slave robots by using assist control for human operators. In this paper, we tackle a reaching task performed by a master-slave robot and propose an operation assist algorithm based on visual feedback control. The algorithm consists...
Spatio-temporal data is intrinsically high dimensional, so unsupervised modeling is only feasible if we can exploit structure in the process. When the dynamics are local in both space and time, this structure can be exploited by splitting the global field into many lower-dimensional “light cones”. We review light cone decompositions for predictive state reconstruction, introducing three simple light...
Nearly all existing estimations of the central subspace in regression take the frequentist approach. However, when the predictors fall naturally into a number of groups, these frequentist methods treat all predictors indiscriminately and can result in loss of the group-specific relation between the response and the predictors. In this article, we propose a Bayesian solution for dimension reduction...
In order to increase the tracking performance of ballistic targets, various estimation algorithms have been implemented in the literature. Extended Kalman Filter is one of the most widely used estimation algorithm which uses the nonlinear system and measurement models and linearization methods to estimate the state and state covariances. In the first part of this study, a ballistic coefficient state...
In this paper, we develop an autocovariance-based method for estimating plant-model mismatch in unconstrained model predictive control systems using discrete-time, linear time-invariant state space models. We rely on knowledge of the process noise model, together with other reasonable assumptions, to derive an explicit expression for the autocovariance matrix of the closed-loop outputs. Then, we prove...
This paper describes a quality model for HTTP Adaptive Streaming. It integrates existing audio and video quality scores to a final quality estimation, factoring in quality variations over time, the recency effect, as well as location and length of buffering events at the player side. We built the model based on data gathered from more than 17 subjective quality tests. It was submitted to the ITU-T...
We herein propose an evolutionary multi-agent system (EMAS for short) to build an ensemble of surrogates for prediction. In our EMAS, we employ six kinds of basic surrogates, including Gaussian process, Kriging model, polynomial response surface, radial basis function, radial basis function neural network, and support vector regression machine. We define each surrogate as one agent and co-evolve parameters...
Estimation of precipitation is necessary for optimum utilization of water resources and their appropriate management. The economy of India being heavily dependent on agriculture becomes vulnerable due to lack of adequate irrigation facilities. In this paper, a multiple linear regression model has been developed to reckon annual precipitation over Cuttack district, Odisha, India. The model forecasts...
It is well recognized that effort estimation is an essential part of successful software management. Among many estimation models, the Case-Base Effort Estimation (CBEE) has been intensively used among researchers and practitioners as a promising model for better and accurate effort prediction. The common challenges with this model are: (1) finding the nearest cases to the new case, (2) selecting...
This paper aims to study the use of a multistep parallel-strategy (MSPS) for long-term estimation of the remaining useful life of the Li-ion batteries. Various extreme learning machines (ELMs) including standard ELM, kernel ELMs and online sequential ELM (OS-ELM) are used along with the parallel strategy for multi-step prognosis. These multistep predictors are trained by means of constant current...
Power load estimation, especially short-term power load estimation, plays an important role in the management of a power system in terms of system security and electricity costs. Therefore, estimation of short-term power load accurately is a popular research issue. In this paper, the generalized behavioral learning method (GBLM), a method developed based on human's behavioral learning theories, was...
Users' addiction to online social networks is discovered to be highly correlated with their social connections in the networks. Dense social connections can effectively help online social networks retain their active users and improve the social network services. Therefore, it is of great importance to make a good prediction of the social links among users. Meanwhile, to enjoy more social network...
We introduce a bi-objective effort estimation algorithm that combines Confidence Interval Analysis and assessment of Mean Absolute Error. We evaluate our proposed algorithm on three different alternative formulations, baseline comparators and current state-of-the-art effort estimators applied to five real-world datasets from the PROMISE repository, involving 724 different software projects in total...
Friend recommendation has been one of the most challenging problems as the social networks grow rapidly, due to the needs of seeking people who are acquaintances in real life or share the common interests. In this paper, we tackle the problem by treating it as a link prediction task and propose a hybrid algorithm that exploits the existing friendship links, users' history ratings and the tags annotated...
In this paper, we propose a variable forgetting factor-based local average model for estimation of future values of financial time series. The forgetting factor is applied to the existing local average model to govern the weights of past records for the estimation of the future records. By using the trend direction from the turning points of the financial time series, the value of the forgetting factor...
In scaled lasso, the unknown regression coefficients and the scale parameter of the error distribution are estimated jointly. In lasso, the optimal penalty parameter is well-known to depend on the error scale, and it is therefore typically chosen using cross-validation. The main benefit of scaled lasso is that the penalty parameter is scale-free and can be predetermined from pure theoretical considerations...
Fast and accurate performance estimation is a key challenge in modern system design. Recently, machine learning-based approaches have emerged that allow predicting the performance of an application on a target platform from executions on a different host. However, existing approaches rely on expensive instrumentation that requires source code to be available. We propose a novel sampling-based, binary-level...
This paper presents a method for estimating the electrical angle of the rotor for permanent-magnet synchronous motors (PMSMs) in model predictive current control (MPCC) so that an encoder is removed for high reliability and low system costs. In our approach, an estimator of the electrical angle of the rotor is introduced in the MPCC. The effectiveness of the proposed method is verified through simulations...
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