FLECS: A Data-Driven Framework for Rapid Protocol Prototyping

Flecs is a framework for facilitating rapid implementation of communication protocols. Forwarding functionality of protocols can be modeled as a combination of packet processing components called abstract switching elements or Ases. The design of Ases is constrained by the axioms of communication which enables us to formally analyze forwarding mechanisms in communication networks. Ases can be connected in a directed graph to define complex forwarding functionality. We have developed Flecs on top of the Click modular router. The compilers in the Flecs framework translate protocol specifications into its Click implementation. We claim that the use of our framework reduces the implementation time by allowing the programmer to specify Ases and the forwarding configuration in a high-level meta-language and produces reasonably efficient implementations. It allows rapid prototyping through configuration, as well as specialized implementation of performance-critical functionality through inheritance

Social relationship analysis using state-of-the-art embeddings.

Detection of human relationships from their interactions on social media is a challenging problem with a wide range of applications in different areas, like targeted marketing, cyber-crime, fraud, defense, planning, and human resource, to name a few. All previous work in this area has only dealt with the most basic types of relationships. The proposed approach goes beyond the previous work to efficiently handle the hierarchy of social relationships. This article introduces a novel technique named Quantifiable Social Relationship (QSR) analysis for quantifying social relationships to analyze relationships between agents from their textual conversations. QSR uses cross-disciplinary techniques from computational linguistics and cognitive psychology to identify relationships. QSR utilizes sentiment and behavioral styles displayed in the conversations for mapping them onto level II relationship categories. Then, for identifying the level III relationship categories, QSR uses level II relationships, sentiments, interactions, and word embeddings as key features. QSR employs natural language processing techniques for feature engineering and state-of-the-art embeddings generated by word2vec, global vectors (glove), and bidirectional encoder representations from transformers (bert). QSR combines the intrinsic conversational features with word embeddings for classifying relationships. QSR achieves an accuracy of up to 89% for classifying relationship subtypes. The evaluation shows that QSR can accurately identify the hierarchical relationships between agents by extracting intrinsic and extrinsic features from textual conversations between agents

Understanding Citizen Issues through Reviews: A Step towards Data Informed Planning in Smart Cities

Governments these days are demanding better Smart City technologies in order to connect with citizens and understand their demands. For such governments, much needed information exists on social media where members belonging to diverse groups share different interests, post statuses, review and comment on various topics. Aspect extraction from this data can provide a thorough understanding of citizens’ behaviors and choices. Also, categorization of these aspects can better summarize societal concerns regarding political, economic, religious and social issues. Aspect category detection (ACD) from people reviews is one of the major tasks of aspect-based sentiment analysis (ABSA). The success of ABSA is mainly defined by the inexpensive and accurate machine-processable representation of the raw input sentences. Previous approaches rely on cumbersome feature extraction procedures from sentences, which adds its own complexity and inaccuracy in performing ACD tasks. In this paper, we propose an inexpensive and simple method to obtain the most suitable representation of a sentence-vector through different algebraic combinations of a sentence’s word vectors, which will act as an input to any machine learning classifier. We have tested our technique on the restaurant review data provided in SemEval-2015 and SemEval-2016. SemEval is a series of global challenges to evaluate the effectiveness of disambiguation of word sense. Our results showed the highest F1-scores of 76.40% in SemEval-2016 Task 5, and 94.99% in SemEval-2015 Task 12