Soft quantification in statistical relational learning

We present a new statistical relational learning (SRL) framework that supports reasoning with soft quantifiers, such as "most" and "a few." We define the syntax and the semantics of this language, which we call , and present a most probable explanation inference algorithm for it. To the best of our knowledge, is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for two real-world applications, link prediction in social trust networks and user profiling in social networks, demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves inference accuracy

Computational personality recognition in social media

A variety of approaches have been recently proposed to automatically infer users' personality from their user generated content in social media. Approaches differ in terms of the machine learning algorithms and the feature sets used, type of utilized footprint, and the social media environment used to collect the data. In this paper, we perform a comparative analysis of state-of-the-art computational personality recognition methods on a varied set of social media ground truth data from Facebook, Twitter and YouTube. We answer three questions: (1) Should personality prediction be treated as a multi-label prediction task (i.e., all personality traits of a given user are predicted at once), or should each trait be identified separately? (2) Which predictive features work well across different on-line environments? (3) What is the decay in accuracy when porting models trained in one social media environment to another

Vector-Based Semantic Expansion Approach: An Application to Patent Retrieval

Patent collection is increasing incrementally. Most of the new technological information is from patent documents, and retrieving specific patents in such a large pool of documents has become a challenging issue for both patent examiners and normal/inexperienced users. Nowadays, most of the retrieval systems (patent retrieval systems) search for the exact match of the query string inside the collection to return a result. However, based on natural language processing, most words have different meanings and each concept can be expressed by more than one word. Since patent documents include lots of new technical terms, searching among them by exact match needs lots of experiences and it is an exhausting task. In this study we aim to investigate this problem by using expansion techniques. We propose a new semantic expansion approach, namely, vector-based semantic expansion (VSE). To do the expansion, we use external knowledge sources, WordNet and Wikipedia. We also examine the affects of using their combination on the expansion results. We do word (VSWE), query (VSQE), and document (VSDE) expansion in this thesis to demonstrate the performance of our approach. We show that our approach with combination of these two knowledge sources can be effective to find similarity between two units of language which might be word, sentence or text. We apply our technique on Miller and Charles dataset to find word-word similarity. Also, our experiments which are based on clef-ip 2011 shows that our technique increases the recall-rate in query expansion, especially for shorter queries.Computer science, Information Architecture (IA) track, Web information systems (WIS) groupSoftware and Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc

Multimodal mixture density boosting network for personality mining

© Springer International Publishing AG, part of Springer Nature 2018. Knowing people’s personalities is useful in various real-world applications, such as personnel selection. Traditionally, we have to rely on qualitative methodologies, e.g. surveys or psychology tests to determine a person’s traits. However, recent advances in machine learning have it possible to automate this process by inferring personalities from textual data. Despite of its success, text-based method ignores the facial expression and the way people speak, which can also carry important information about human characteristics. In this work, a personality mining framework is proposed to exploit all the information from videos, including visual, auditory, and textual perspectives. Using a state-of-art cascade network built on advanced gradient boosting algorithms, the result produced by our proposed methodology can achieve lower the prediction errors than most current machine learning algorithms. Our multimodal mixture density boosting network especially perform well with small sample size datasets, which is useful for learning problems in psychology fields where big data is often not available

Sustainable Services to Enhance Flexibility in the Upcoming Smart Grids

Global efforts are already focusing on future targets for even more increases in renewable energy sources contribution, greater efficiency improvements and further greenhouse gas emission reductions. With the fast-paced changing technologies in the context of sustainable development, new approaches and concepts are needed to cope with the variability and uncertainty affecting generation, transmission and load demand. The main challenge remains in developing technologies that can efficiently make use of the available renewable resources. Alternatives in the form of microgrids or virtual power plants along with electricity storage are potential candidates for enhancing flexibility. However, intelligence must be added at all levels in the grid and among all the equipment comprising each subsystem, in order to achieve two-way communications and bidirectional flow of power. Then, the concept of smart grid can be realized and, relying upon software systems, it can remotely and automatically dispatch and optimize generation or storage resources in a single, secure and Web-connected way. Deploying smart configurations and metering promises new possibilities for self-managed energy consumption, improved energy efficiency among final consumers and transition to more consumer-centric energy systems via demand response and demand-side management mechanisms