Many social networks are characterized by actors (nodes) holding quantitative opinions about movies, songs, sports, people, colleges, politicians, and so on. These influenced network neighbors. models have been proposed for such opinion dynamics, but they some limitations. Most consider the strength of edge influence as fixed. Some model a discrete decision or action on part each actor, an causing ``infection'' (that is often permanent self-resolving). Others stochastic matrix to reuse...

Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an target or aspect, its associated sentiment, and the corresponding term/span explaining rationale behind sentiment. Existing research efforts are majorly tagging-based. Among methods taking a sequence tagging approach, some fail to capture strong interdependence between three factors, whereas others fall short identifying triplets overlapping aspect/opinion spans. A recent grid approach on...

Near infrared photoacoustic spectroscopy is utilized for the development of a continuous non-invasive glucose monitoring system diabetics. A portable embedded taking measurements on tissues to estimate concentration implemented using field programmable gate array (FPGA). The back-end architecture high-speed data acquisition and de-noising operates at 274.823 MHz Xilinx Virtex-II Pro FPGA. measurement technique verified in vitro solutions vivo tissues, with signal amplitude varying linearly...

Predicting plausible links that may emerge between pairs of nodes is an important task in social network analysis, with over a decade active research. Here, we propose novel framework for link prediction. It integrates signals from node features, the existing local neighborhood pair, community-level density, and global graph properties. Our uses stacked two-level learning paradigm. At lower level, first two kinds features are processed by learner. Its outputs then integrated last...

Solar flares create adverse space weather impacting and Earth-based technologies. However, the difficulty of forecasting flares, by extension severe weather, is accentuated lack any unique flare trigger or a single physical pathway. Studies indicate that multiple properties contribute to active region potential, compounding challenge. Recent developments in machine learning (ML) have enabled analysis higher-dimensional data leading increasingly better techniques. consensus on high-performing...

Manually extracting relevant aspects and opinions from large volumes of user-generated text is a time-consuming process. Summaries, on the other hand, help readers with limited time budgets to quickly consume key ideas data. State-of-the-art approaches for multi-document summarization, however, do not consider user preferences while generating summaries. In this work, we argue need propose solution personalized aspect-based opinion summaries collections online tourist reviews. We let our...

Occurrences of catastrophes such as natural or man-made disasters trigger the spread rumours over social media at a rapid pace. Presenting trustworthy and summarized account unfolding event in near real-time to consumers potentially unreliable information thus becomes an important task. In this work, we propose MTLTS, first end-to-end solution for task that jointly determines credibility summary-worthiness tweets. Our verifier is designed recursively learn structural properties Twitter...

Segmentation of a string English language characters into sequence words has many applications. Here, we study two applications in the internet domain. First application is web domain segmentation which crucial for monetization broken URLs. Secondly, propose and novel twitter hashtag increasing recall on searches. Existing methods word use unsupervised models. We find that when using multiple corpora, joint probability model from corpora performs significantly better than individual corpora....

A large fraction of data generated via human activities such as online purchases, health records, spatial mobility etc. can be represented a sequence events over continuous-time. Learning deep learning models these continuous-time event sequences is non-trivial task it involves modeling the ever-increasing timestamps, inter-event time gaps, types, and influences between different within across sequences. In recent years neural enhancements to marked temporal point processes (MTPP) have...

The networked opinion diffusion in online social networks (OSN) is governed by the two genres of opinions-endogenous opinions that are driven influence contacts between users, and exogenous which formed external effects like news, feeds etc. Such duplex dynamics led users belonging to categories- organic who generally post endogenous extrinsic susceptible externalities, mostly messages. Precise demarcation messages offers an important cue modeling, thereby enhancing its predictive...

Social networks, forums, and social media have emerged as global platforms for forming shaping opinions on a broad spectrum of topics like politics, sports, entertainment. Users (also called actors ) often update their evolving opinions, influenced through discussions with other users. Theoretical models analysis understanding opinion dynamics in networks abound the literature. However, these are based concepts from statistical physics. Their goal is to establish specific phenomena steady...

Aspect level sentiment classification (ALSC) is a difficult problem with state-of-the-art models showing less than 80% macro-F1 score on benchmark datasets. Existing do not incorporate information aspect-aspect relations in knowledge graphs (KGs), e.g. DBpedia. Two main challenges stem from inaccurate disambiguation of aspects to KG entities, and the inability learn aspect representations large KGs joint training ALSC models. We propose AR-BERT, novel two-level global-local entity embedding...

Online Social Networks (OSNs) have emerged as a global media for forming and shaping opinions on broad spectrum of topics like politics, e-commerce, sports, etc. So, research understanding predicting opinion dynamics in OSNs, especially using tractable linear model, has abound literature. However, these models are too simple to uncover the actual complex flow social networks. In this paper, we propose SLANT+, novel nonlinear generative model dynamics, by extending our earlier SLANT [7]. To...

Named entity disambiguation (NED) is a central problem in information extraction. The goal to link entities knowledge graph (KG) their mention spans unstructured text. Each distinct span (like John Smith, Jordan or Apache) represents multi-class classification task. NED can therefore be modeled as multitask with tens of millions tasks for realistic KGs. We initiate an investigation into neural representations, network architectures, and training protocols NED. Specifically, we propose...

This article presents a new fast, highly scalable distributed matrix multiplication algorithm on Apache Spark, called <i>Stark</i> , based Strassen's algorithm. Stark preserves seven multiplications scheme in environment and thus achieves asymptotically faster execution time. It creates recursion tree of computation where each level the corresponds to division combination blocks stored form Resilient Distributed Datasets (RDDs). processes divide combine step parallel memorises sub-matrices...

Social media and social networking sites have become a global pinboard for exposition discussion of news, topics, ideas, where users often update their opinions about particular topic by learning from the shared friends. In this context, can we learn data-driven model opinion dynamics that is able to accurately forecast users? paper, introduce SLANT, probabilistic modeling framework dynamics, which represents over time means marked jump diffusion stochastic differential equations, allows...

The growth of big data in domains such as Earth Sciences, Social Networks, Physical etc. has lead to an immense need for efficient and scalable linear algebra operations, e.g. Matrix inversion. Existing methods distributed matrix inversion using platforms rely on LU decomposition based block-recursive algorithms. However, these algorithms are complex require a lot side calculations, multiplication, at various levels recursion. In this paper, we propose different scheme Strassen's algorithm...

Structural alignments are the most widely used tools for comparing proteins with low sequence similarity. The main contribution of this paper is to derive various kernels on from structural alignments, which do not use information. Central a novel alignment algorithm matches substructures fixed size using spectral graph matching techniques. We positive semi-definite capture notion similarity between substructures. Using these as base more sophisticated protein structures proposed. To...