Abstract

INDIA IS A software superpower today. This achievement rests on more than four decades of work spanning software processes, rigorous engineering and value-adding technologies, among others. In this article, we present highlights of some of these activities. This regional section also contains other articles that complement this account of exciting work in software systems stemming from India.

Abstract

We study the problem of a buyer who gains stochastic rewards by procuring through an auction, multiple units of a service or item from a pool of heterogeneous agents who are strategic on two dimensions, namely cost and capacity. The reward obtained for a single unit from an allocated agent depends on the inherent quality of the agent; the agent’s quality is fixed but unknown. Each agent can only supply a limited number of units (capacity of the agent). The cost incurred per unit and capacity (maximum number of units that can be supplied) are private information of each agent. The auctioneer is required to elicit from the agents their costs as well as capacities (making the mechanism design bidimensional) and further, learn the qualities of the agents as well, with a view to maximize her utility. Motivated by this, we design a bidimensional multi-armed bandit procurement auction that seeks to maximize the

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

Social media platforms such as Twitter and Facebook are becoming popular in multilingual societies. This trend induces portmanteau of South Asian languages with English. The blend of multiple languages as code-mixed data has recently become popular in research communities for various NLP tasks. Code-mixed data consist of anomalies such as grammatical errors and spelling variations. In this paper, we leverage the contextual property of words where the different spelling variation of words share similar context in a large noisy social media text. We capture different variations of words belonging to same context in an unsupervised manner using distributed representations of words. Our experiments reveal that preprocessing of the code-mixed dataset based on our approach improves the performance in state-of-the-art part-of-speech tagging (POS-tagging) and sentiment analysis tasks.

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.