A variety of approaches have been proposed to automatically infer the profiles of users from their digital footprint in social media. Most of the proposed approaches focus on mining a single type of information, while ignoring other sources of available user-generated content (UGC). In this paper, we propose a mechanism to infer a variety of user characteristics, such as, age, gender and personality traits, which can then be compiled into a user profile. To this end, we model social media users by incorporating and reasoning over multiple sources of UGC as well as social relations. Our model is based on a statistical relational learning framework using Hinge-loss Markov Random Fields (HL-MRFs), a class of probabilistic graphical models that can be defined using a set of first-order logical rules. We validate our approach on data from Facebook with more than 5k users and almost 725k relations. We show how HL-MRFs can be used to develop a generic and extensible user profiling framework by leveraging textual, visual, and relational content in the form of status updates, profile pictures and Facebook page likes. Our experimental results demonstrate that our proposed model successfully incorporates multiple sources of information and outperforms competing methods that use only one source of information or an ensemble method across the different sources for modeling of users in social media.

For the past decades we have experienced an enormous expansion of the accumulated data that humanity produces. Daily a numerous number of smart devices, usually interconnected over internet, produce vast, real-values datasets. Time series representing datasets from completely irrelevant domains such as finance, weather, medical applications, traffic control etc. become more and more crucial in human day life. Analyzing and clustering these time series, or in general any kind of curves, could be critical for several human activities. In the current paper, the new Curves Clustering Using Common Patterns (3CP) methodology is introduced, which applies a repeated pattern detection algorithm in order to cluster sequences according to their shape and the similarities of common patterns between time series, data curves and eventually any kind of discrete sequences. For this purpose, the Longest Expected Repeated Pattern Reduced Suffix Array (LERP-RSA) data structure has been used in combination with the All Repeated Patterns Detection (ARPaD) algorithm in order to perform highly accurate and efficient detection of similarities among data curves that can be used for clustering purposes and which also provides additional flexibility and features.

Transportation occupies one-third of the amount in the logistics costs, and accordingly transportation systems largely influence the performance of the logistics system. This work presents an adaptive data-driven innovative modular approach for solving the real-world Vehicle Routing Problems (VRP) in the field of logistics. The work consists of two basic units: (i) an innovative multi-step algorithm for successful and entirely feasible solving of the VRP problems in logistics, (ii) an adaptive approach for adjusting and setting up parameters and constants of the proposed algorithm. The proposed algorithm combines several data transformation approaches, heuristics and Tabu search. Moreover, as the performance of the algorithm depends on the set of control parameters and constants, a predictive model that adaptively adjusts these parameters and constants according to historical data is proposed. A comparison of the acquired results has been made using the Decision Support System with predictive models: Generalized Linear Models (GLM) and Support Vector Machine (SVM). The algorithm, along with the control parameters, which using the prediction method were acquired, was incorporated into a web-based enterprise system, which is in use in several big distribution companies in Bosnia and Herzegovina. The results of the proposed algorithm were compared with a set of benchmark instances and validated over real benchmark instances as well. The successful feasibility of the given routes, in a real environment, is also presented.

In this tutorial you are going to learn about the k-Nearest Neighbors algorithm including how it works and how to implement it from scratch in Python (without libraries). A simple but powerful approach for making predictions is to use the most similar historical examples to the new data. This is the principle behind the k-Nearest Neighbors algorithm. Discover how to code ML algorithms from scratch including kNN, decision trees, neural nets, ensembles and much more in my new book, with full Python code and no fancy libraries. Develop k-Nearest Neighbors in Python From Scratch Image taken from Wikipedia, some rights reserved.

In this tutorial, we are discussing Object Function (Least Square Method). The Least Square method is a parameter estimation method, used widely in engineering and across nearly all fields of science. This method attempts to determine the mathematical relationship between the dependent value and the physical quantity. The optimal method for solving the regression problem is the LS method. Call Us on 91 90191 91856 Website https://www.mytectra.com

In this tutorial, we are discussing Object Function (Least Square Method). The Least Square method is a parameter estimation method, used widely in engineering and across nearly all fields of science. This method attempts to determine the mathematical relationship between the dependent value and the physical quantity. The optimal method for solving the regression problem is the LS method. Call Us on 91 90191 91856 Website https://www.mytectra.com

Everyone who has ever worked on an application development project knows that you don't just simply put code and content out in production, to your customers, employees, or stakeholders without first testing it to make sure it's not broken or dead on delivery. Quality Assurance (QA) is such a core part of any technology or business delivery that it's one of the essential components of any development methodology. And the best way to do all this is in an agile fashion, in small, iterative chunks so you make sure to respond to the continuously evolving and changing needs of the customer. Surely AI projects are no different. There are iterative design, development, testing, and delivery phases, as we've discussed in our previous content on AI methodologies.

We propose a new framework of CatBoost that predicts the entire conditional distribution of a univariate response variable. In particular, CatBoostLSS models all moments of a parametric distribution (i.e., mean, location, scale and shape [LSS]) instead of the conditional mean only. Choosing from a wide range of continuous, discrete and mixed discrete-continuous distributions, modelling and predicting the entire conditional distribution greatly enhances the flexibility of CatBoost, as it allows to gain insight into the data generating process, as well as to create probabilistic forecasts from which prediction intervals and quantiles of interest can be derived. We present both a simulation study and real-world examples that demonstrate the benefits of our approach.

This is probably due to at least two reasons: high volatility of the Bitcoin price and market immaturity for cryptocurrencies. This is confirmed by the statistics reported in tables 1 and 2. The results obtained partitioning the dataset into shorter sequences also confirmed the kindness of our hypothesis of identifying time regimes that do not resemble a random walk and that are easier to model, finding that best results are obtained using more than one previous price. It is worth noting that, with this novel approach we obtained the best results for the Bitcoin price series, rather than for the stock market series as happened in the analysis of the series in their totality. As stated before, this is probably 18 due to the high volatility of the Bitcoin price, in fact it is no accident that the best result was found for the time regime identified by a translation step h of 120, where the Bitcoin prices are more distributed around the mean, showing a lower variance. This is confirmed by the standard deviation values shown in table 2. It is important to emphasize that the innovative approach proposed in this paper, namely the identification of short-time regimes within the entire series, allowed us to obtain leading-edge results in the field of financial series forecasting.

The recent increase in the scale and complexity of software systems has introduced new challenges to the time series monitoring and anomaly detection process. A major drawback of existing anomaly detection methods is that they lack contextual information to help stakeholders identify the cause of anomalies. This problem, known as root cause detection, is particularly challenging to undertake in today's complex distributed software systems since the metrics under consideration generally have multiple internal and external dependencies. Significant manual analysis and strong domain expertise is required to isolate the correct cause of the problem. In this paper, we propose a method that isolates the root cause of an anomaly by analyzing the patterns in time series fluctuations. Our method considers the time series as observations from an underlying process passing through a sequence of discretized hidden states. The idea is to track the propagation of the effect when a given problem causes unaligned but homogeneous shifts of the underlying states. We evaluate our approach by finding the root cause of anomalies in Zillows clickstream data by identifying causal patterns among a set of observed fluctuations.