Towards Deep Semantic Analysis Of Hashtags

Hashtags are semantico-syntactic constructs used across various social networking and microblogging platforms to enable users to start a topic specific discussion or classify a post into a desired category. Segmenting and linking the entities present within the hashtags could therefore help in better understanding and extraction of information shared across the social media. However, due to lack of space delimiters in the hashtags (e.g #nsavssnowden), the segmentation of hashtags into constituent entities ("NSA" and "Edward Snowden" in this case) is not a trivial task. Most of the current state-of-the-art social media analytics systems like Sentiment Analysis and Entity Linking tend to either ignore hashtags, or treat them as a single word. In this paper, we present a context aware approach to segment and link entities in the hashtags to a knowledge base (KB) entry, based on the context within the tweet. Our approach segments and links the entities in hashtags such that the coherence between hashtag semantics and the tweet is maximized. To the best of our knowledge, no existing study addresses the issue of linking entities in hashtags for extracting semantic information. We evaluate our method on two different datasets, and demonstrate the effectiveness of our technique in improving the overall entity linking in tweets via additional semantic information provided by segmenting and linking entities in a hashtag.Comment: To Appear in 37th European Conference on Information Retrieva

Rotated multifractal network generator

The recently introduced multifractal network generator (MFNG), has been shown to provide a simple and flexible tool for creating random graphs with very diverse features. The MFNG is based on multifractal measures embedded in 2d, leading also to isolated nodes, whose number is relatively low for realistic cases, but may become dominant in the limiting case of infinitely large network sizes. Here we discuss the relation between this effect and the information dimension for the 1d projection of the link probability measure (LPM), and argue that the node isolation can be avoided by a simple transformation of the LPM based on rotation.Comment: Accepted for publication in JSTA

RECOLED: A group-aware collaborative text editor for capturing document history

This paper presents a usability analysis of RECOLED, a shared document editor which supports recording of audio communication in remote collaborative writing sessions, and transparent monitoring of interactions, such as editing, gesturing and scrolling. The editor has been designed so that the collaboration results in the production of a multimedia document history which enriches the final product of the writing activity and can serve as a basis for post-meeting information retrieval. A discussion is presented on how post-meeting processing can highlight the usefulness of such histories in terms of tracking information that would be normally lost in usual collaborative editing settings

Reducing Timing Interferences in Real-Time Applications Running on Multicore Architectures

We introduce a unified wcet analysis and scheduling framework for real-time applications deployed on multicore architectures. Our method does not follow a particular programming model, meaning that any piece of existing code (in particular legacy) can be re-used, and aims at reducing automatically the worst-case number of timing interferences between tasks. Our method is based on the notion of Time Interest Points (tips), which are instructions that can generate and/or suffer from timing interferences. We show how such points can be extracted from the binary code of applications and selected prior to performing the wcet analysis. We then represent real-time tasks as sequences of time intervals separated by tips, and schedule those tasks so that the overall makespan (including the potential timing penalties incurred by interferences) is minimized. This scheduling phase is performed using an Integer Linear Programming (ilp) solver. Preliminary results on state-of-the-art benchmarks show promising results and pave the way for future extensions of the model and optimizations

A lightweight web video model with content and context descriptions for integration with linked data

The rapid increase of video data on the Web has warranted an urgent need for effective representation, management and retrieval of web videos. Recently, many studies have been carried out for ontological representation of videos, either using domain dependent or generic schemas such as MPEG-7, MPEG-4, and COMM. In spite of their extensive coverage and sound theoretical grounding, they are yet to be widely used by users. Two main possible reasons are the complexities involved and a lack of tool support. We propose a lightweight video content model for content-context description and integration. The uniqueness of the model is that it tries to model the emerging social context to describe and interpret the video. Our approach is grounded on exploiting easily extractable evolving contextual metadata and on the availability of existing data on the Web. This enables representational homogeneity and a firm basis for information integration among semantically-enabled data sources. The model uses many existing schemas to describe various ontology classes and shows the scope of interlinking with the Linked Data cloud

From Professional Business Partner to Strategic Talent Leader : What’s Next for Human Resource Management

The HR profession is at a critical inflection point. It can evolve into a true decision science of talent, and aspire to the level of influence of disciplines such as Finance and Marketing, or it can continue the traditional focus on support services and program delivery to organizational clients. In this paper, we suggest that the transition to a decision science is essential and not only feasible, but historically predictable. However, we show that making the transition is not a function of achieving best-practice professional practices. Rather, it requires developing a logical, deep and coherent framework linking organizational talent to strategic success. We show how the evolution of the decision sciences of Finance and Marketing, out of the professional practices of Accounting and Sales, provide the principles to guide the evolution from the current professional practice of HR, to the emerging decision science of talentship

Topological effects in the thermal properties of knotted polymer rings

The topological effects on the thermal properties of several knot configurations are investigated using Monte Carlo simulations. In order to check if the topology of the knots is preserved during the thermal fluctuations we propose a method that allows very fast calculations and can be easily applied to arbitrarily complex knots. As an application, the specific energy and heat capacity of the trefoil, the figure-eight and the $8_1$ knots are calculated at different temperatures and for different lengths. Short-range repulsive interactions between the monomers are assumed. The knots configurations are generated on a three-dimensional cubic lattice and sampled by means of the Wang-Landau algorithm and of the pivot method. The obtained results show that the topological effects play a key role for short-length polymers. Three temperature regimes of the growth rate of the internal energy of the system are distinguished.Comment: 7 pages, 12 figures, LaTeX + RevTeX. With respect to the first version, in the second version the text has been improved and all figures are now in black and whit