Abstract

eterson’s Mutual Exclusion Algorithm as Feedback Control

Abstract

Software systems often serve as the agents of operation for both enterprise systems and embedded systems. Engineering such systems is a knowledge-centric activity. A clear understanding of the relationship between knowledge, systems and engineering can help us to establish firm theoretical foundations for software and systems engineering. Currently we have a strong intuitive understanding of how knowledge flows into engineering, while our understanding of the relationship between systems and knowledge is part explicit and part tacit. A symptom of this is that we have difficulty in building unified models of large systems such as telescopes and enterprises that span multiple knowledge domains and viewpoints. We are able to build multiple models covering various aspects and particular viewpoints. However, we have challenges in integrating them into a single unified model. Another symptom is that software and systems engineering practice are widely viewed as empirical fields, without sufficiently strong theoretical foundations. This work attempts to explicate and synthesize our common intuitive understanding in this space to develop a conceptual model of the relationships. It then explores the validity of this model by examining the extent to which it is able to explain and illuminate current engineering practices and issues. This is an initial strawman version of the model, presented with a view to obtaining feedback and inputs from the community.

Abstract

Software systems often serve as the agents of operation for both enterprise systems and embedded systems. Engineering such systems is a knowledge-centric activity. A clear understanding of the relationship between knowledge, systems and engineering can help us to establish firm theoretical foundations for software and systems engineering. Currently we have a strong intuitive understanding of how knowledge flows into engineering, while our understanding of the relationship between systems and knowledge is part explicit and part tacit. A symptom of this is that we have difficulty in building unified models of large systems such as telescopes and enterprises that span multiple knowledge domains and viewpoints. We are able to build multiple models covering various aspects and particular viewpoints. However, we have challenges in integrating them into a single unified model. Another symptom is that software and systems engineering practice are widely viewed as empirical fields, without sufficiently strong theoretical foundations. This work attempts to explicate and synthesize our common intuitive understanding in this space to develop a conceptual model of the relationships. It then explores the validity of this model by examining the extent to which it is able to explain and illuminate current engineering practices and issues. This is an initial strawman version of the model, presented with a view to obtaining feedback and inputs from the community.

Abstract

Sentences with high structural complexity prove to be calamitous for applications relying on natural language processing. An increasing level of intricateness or convolution in the sentence pushes the performance of NLP systems to the downgrade. These systems stand to make advantage of the process that can reduce the structural complexity of a sentence. Most of the work done on simplifying sentences of Hindi is based on rules based approaches. This work is not only limited and dependent on other resources but also prone to produce erroneous output. In this study we have first come up with improvements in one such rule based approach. The improved version of this approach is then used to produce a parallel corpus of complex and simple sentences. After manual correction, this corpus is used to perform task of sentence simplification through monolingual machine translation.

Abstract

The proposed work exploits acoustic cues at various levels and incorporates them in the present (Spoken Term Detection) STD frame work. Recently proposed new syllabification method [1] for speech signal is being used for STD. In STD, a query and reference speech signals are provided, these speech signals are syllabified using the new syllabification method and features like Mel-frequency cepstral coefficients (MFCC), posterior grams are extracted. These features are then matched using template based match techniques like dynamic time warping (DTW) at syllable level instead of regular frame level. This essentially reduces the unwanted matching done at frame level.

Abstract

With the increasing usage of internet, more and more data is being digitized including parliamentary debates but they are in an unstructured format. There is a need to convert them into structured format for linguistic analysis. Much work has been done on parliamentary data such as Hansard, American congressional floor-debate data on various aspects but less on pragmatics. In this paper, we provide a dataset for synopsis of Indian parliamentary debates and perform stance classification of speeches i.e identifying if the speaker is supporting the bill/issue or is against it. We also analyze the intention of the speeches beyond mere sentences i.e pragmatics in the parliament. Based on thorough manual analysis of the debates, we developed an annotation scheme of 4 mutually exclusive categories to analyze the purpose of

Abstract

The paper describes the enrichment of OntoSenseNet- a verb- centric lexical resource for Indian Languages. This resource contains a newly developed Telugu-Telugu dictionary. It is important because na- tive speakers can better annotate the senses when both the word and its meaning are in Telugu. Hence efforts are made to develop a soft copy of Telugu dictionary. Our resource also has manually annotated gold standard corpus consisting 8483 verbs, 253 adverbs and 1673 adjectives. Annotations are done by native speakers according to defined annotation guidelines. In this paper, we provide an overview of the annotation proce- dure and present the validation of our resource through inter-annotator agreement. Concepts of sense-class and sense-type are discussed. Addi- tionally, we discuss the potential of lexical sense-annotated corpora in improving word sense disambiguation (WSD) tasks. Telugu WordNet is crowd-sourced for annotation of individual words in synsets and is compared with the developed sense-annotated lexicon (OntoSenseNet) to examine the improvement. Also, we present a special categorization (spatio-temporal classification) of adjectives.

Abstract

CESNA model consists of twin Bi-directional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM RNN) with shared parameters joined by a contrastive loss function based on a similarity metric. The model learns the representations of resource-poor and resource-rich language in a common emoji space by using a similarity metric based on the emojis present in sentences from both languages. The model, hence, projects sentences with similar emojis closer to each other and the sentences with different emojis farther from one another. Experiments on large-scale Twitter datasets of resource-rich languages - English and Spanish and resource-poor languages - Hindi and Telugu reveal that CESNA outperforms the state-of-the-art emoji prediction approaches based on distributional semantics,

Abstract

Following approaches for understanding lexical meaning de- veloped by Y¯aska, Patanjali and Bhartrihari from Indian linguistic tra- ditions and extending approaches developed by Leibniz and Brentano in the modern times, a framework of formal ontology of language was devel- oped. This framework proposes that meaning of words are in-formed by intrinsic and extrinsic ontological structures. The paper aims to capture such intrinsic and extrinsic meanings of words for two major Indian lan- guages, namely, Hindi and Telugu. Parts-of-speech have been rendered into sense-types and sense-classes. Using them we have developed a gold- standard annotated lexical resource to support semantic understanding of a language. The resource has collection of Hindi and Telugu lexicons, which has been manually annotated by native speakers of the languages following our annotation guidelines. Further, the resource was utilised to derive adverbial sense-class distribution of verbs and k¯araka-verb sense- type distribution. Different corpora (news, novels) were compared using verb sense-types distribution. Word Embedding was used as an aid for the enrichment of the resource. This is a work in progress that aims at lexical coverage of language extensively

Abstract

Prediction of trust relations between users of social networks is critical for finding credible information. Inferring trust is challenging, since userspecified trust relations are highly sparse and power-law distributed. In this paper, we explore utilizing community memberships for trust prediction in a principled manner. We also propose a novel method to model homophily that complements existing work. To the best of our knowledge, this is the first work that mathematically formulates an insight based on community memberships for unsupervised trust prediction. We propose and model the hypothesis that a user is more likely to develop a trust relation within the user’s community than outside it. Unlike existing work, our approach for encoding homophily directly links user-user similarities with the pair-wise trust model. We derive mathematical factors that model our hypothesis relating community memberships to trust relations and the homophily effect. Along with low-rank matrix factorization, they are combined into chTrust, the proposed multi-faceted optimization framework. Our experiments on the standard Ciao and Epinions datasets show that the proposed framework outperforms multiple unsupervised trust prediction baselines for all test user pairs as well as the low-degree segment, across evaluation setting