Improving collaborative filtering using lexicon-based sentiment analysis

<span>Since data is available increasingly on the Internet, efforts are needed to develop and improve recommender systems to produce a list of possible favorite items. In this paper, we expand our work to enhance the accuracy of Arabic collaborative filtering by applying sentiment analysis to user reviews, we also addressed major problems of the current work by applying effective techniques to handle the scalability and sparsity problems. The proposed approach consists of two phases: the sentiment analysis and the recommendation phase. The sentiment analysis phase estimates sentiment scores using a special lexicon for the Arabic dataset. The item-based and singular value decomposition-based collaborative filtering are used in the second phase. Overall, our proposed approach improves the experiments’ results by reducing average of mean absolute and root mean squared errors using a large Arabic dataset consisting of 63,000 book reviews.</span>

Does the Type of Records Affect the Estimates of the Parameters?

The maximum likelihood estimation of the unknown parameters of inverse Rayleigh and exponential distributions are discussed based on lower and upper records. The aim is to study the effect of the type of records on the behavior of the corresponding estimators. Mean squared errors are calculated through simulation to study the behavior of the estimators. The results shall be of interest to those situations where the data can be obtained in the form of either of the two types of records and the experimenter must decide between these two for estimation of the unknown parameters of the distribution.

Bayesian Estimation for Two Parameters of Weibull Distribution under Generalized Weighted Loss Function

In this paper, Bayes estimators for the shape and scale parameters of Weibull distribution have been obtained using the generalized weighted loss function, based on Exponential priors. Lindley’s approximation has been used effectively in Bayesian estimation. Based on theMonte Carlo simulation method, those estimators are compared depending on the mean squared errors (MSE’s).

Incidental Parameters Problem: The Case of Gompertz Model

The order of bias of the fixed effects gompertz model is studied, using Monte Carlo approach. Performance criteria are bias and root mean squared errors. For fixed N, bias is found to decrease steadily between T=5 and T=20 but exhibits a mixture of increase and decline afterwards. At each value of T involved, bias steadily decreases with increased value of N. Bias is found to be at most 123%, due to the combination of minimum of each of N and T involved. Decrease in order of bias is found to be more definite with increased N at fixed T than with increased T at fixed N.

Modelling temperature dynamics in sewer systems – comparing mechanistic and conceptual modelling approaches

The vast majority of the energy consumed for urban water services is used to heat tap water. Heat recovery from wastewater is consequently an area of rapidly growing concern, both in research and by commercial interest, promoting the path towards a circular economy. To facilitate a system-wide evaluation of heat recovery from wastewater, this paper compares two one-dimensional models (mechanistic and conceptual) that can describe wastewater temperature dynamics in sewer pipe systems. The models are applied to successfully predict downstream wastewater temperature for sewer stretches in two Swedish cities (Linköping & Malmö). The root mean squared errors for the mechanistic model (Linköping Dataset1 – 0.33 °C; Linköping Dataset2 – 0.28 °C; Malmö – 0.40 °C) and the conceptual model (Linköping Dataset1 – 0.32 °C; Linköping Dataset2 – 0.20 °C; Malmö – 0.44 °C) indicate that both models have similar predictive capabilities, encouraging the use of conceptual models to reduce data requirements and model calibration efforts. Both models are freely distributed and can be easily integrated with wastewater generation and treatment models to facilitate system-wide wastewater temperature dynamics analysis.

CubeSat formation architecture for small space debris surveillance and orbit determination

Introduction: Satellites which face space debris cannot track it throughout the whole orbit due to natural limitations of their optical sensors, sush as field of view, Earth occultation, or solar illumination. Besides, the time of continuous observations is usually very short. Therefore, we are trying to offer the most effective configuration of optical sensors in order to provide short-arc tracking of a target piece of debris, using a scalable Extended Information Filter. Purpose: The best scenario for short-arc tracking of a space debris orbit using multipoint optical sensors. Results: We have found optimal configurations for groups of satellites with optical sensors which move along a sun-synchronous orbit. Debris orbit determination using an Extended Information Filter and measurements from multipoint sensors was simulated, and mean squared errors of the target's position were calculated. Based on the simulation results for variouos configurations, inter-satellite distances and measurement time, the most reliable scenario (four satellites in tetrahedral configuration) was found and recommended for practical use in short-arc debris tracking.

Analyzing Regional and Local Changes in Irradiance during the 2019 Total Solar Eclipse in Chile, Using Field Observations and Analytical Modeling

Solar eclipses are astronomic phenomena in which the Earth’s moon transits between the planet and the Sun, projecting a shadow onto the planet’s surface. As solar power installed capacity increases, detailed studies of this region-wide phenomenon’s effect in irradiance is of interest; however, the literature mainly reports its effects on localized scales. A measurement campaign spanning over 1400 km was pursued for the 2 July 2019 total solar eclipse in Chile, to register the event and establish a modeling framework to assess solar eclipse effects in irradiance over wide regional scales. This work describes the event and presents an estimation framework to decompose atmospheric and eclipse effects on irradiance. An analytical model was applied to study irradiance attenuation throughout the Chilean mainland territory, using satellite-derived and astronomical data as inputs compared to ground measurements in eight stations. Results showed good agreement between model and observations, with Mean Bias Errors of −0.008 to 0.98 W/m2 for Global Horizontal Irradiance and −0.004 to −4.664 W/m2 for Direct Normal Irradiance, with Normalized Root Mean Squared Errors of 0.7–5.8% and 1.4–12.2%, respectively. Energy losses due to obscuration corresponded between 20–40% for Global Horizontal Irradiance and 25–50% for Direct Normal Irradiance over Chilean territory.

Denton’s Temporal Disaggregation Methods with the Application of Bangladesh’s Annual Gross Domestic Product for Quarterly Benchmarking and Forecasting

<p>Different methods of temporal disaggregation are discussed in detail; mainly the methods developed by Denton in 1971 and other purely mathematical methods. First, the original method developed by Denton and its solution are described by referencing Denton’s original article. The Cholette–Dagum regression-based method (or Denton-Cholette method) is also included to enrich the comparison. Bangladesh’s annual export figures are then disaggregated into a quarterly series by Denton’s additive and proportional (first and second difference) methods, and the Denton-Cholette additive and proportional (first and second difference) methods by using R package “tempdisagg”. The quarterly imports of capital goods and others (Iron, steel and other base metals; and capital machinery) in Bangladesh are used as the indicator series for the fiscal years FY2009 to FY2019. By comparing the estimated series with the real quarterly exports series, with the aid of root mean squared errors, it is concluded that the Denton-Cholette additive method (the first difference) performs better than the Denton-Cholette’s proportional variants as well as Denton’s additive and proportional variants.<b></b></p>

Denton’s Temporal Disaggregation Methods with the Application of Bangladesh’s Annual Gross Domestic Product for Quarterly Benchmarking and Forecasting

<p>Different methods of temporal disaggregation are discussed in detail; mainly the methods developed by Denton in 1971 and other purely mathematical methods. First, the original method developed by Denton and its solution are described by referencing Denton’s original article. The Cholette–Dagum regression-based method (or Denton-Cholette method) is also included to enrich the comparison. Bangladesh’s annual export figures are then disaggregated into a quarterly series by Denton’s additive and proportional (first and second difference) methods, and the Denton-Cholette additive and proportional (first and second difference) methods by using R package “tempdisagg”. The quarterly imports of capital goods and others (Iron, steel and other base metals; and capital machinery) in Bangladesh are used as the indicator series for the fiscal years FY2009 to FY2019. By comparing the estimated series with the real quarterly exports series, with the aid of root mean squared errors, it is concluded that the Denton-Cholette additive method (the first difference) performs better than the Denton-Cholette’s proportional variants as well as Denton’s additive and proportional variants.<b></b></p>

Estimation of the Parameters of Type-II Discrete Weibull Distribution Under Type-I Censoring

Maximum likelihood and proportion estimators of the parameters of the discrete Weibull type II distribution with type I censored data are discussed. A simulation study is performed to generate data from this distribution for suggested values of its parameters and to get the Maximum likelihood estimates of the parameters numerically. The method of proportions suggested by Khan et al. (1989) is also used to estimate the model&#39;s parameters. Numerical examples are used to perform a comparison study between the two method results according the values of the estimates and their corresponding mean squared errors.