Abstract

The adoption of Machine Learning (ML) in software applications has increased in domains like healthcare, banking and others. leading to coining of the term MLWare applications. However, challenges like diverse code base, complex components, lack of expertise, etc. persist in development and maintenance of these applications. Application of software engineering patterns and practices for the development of MLWare applications can improve maintainability, extensibility, scalability and other software quality parameters. In this paper, we propose an approach for developing MLWare applications using a pattern oriented approach. We demonstrate the approach on a credit risk scorecard application that can helps loan officer identify risk patterns and make loan decisions.

Abstract

Software quality has become the lever of differentiation in today’s competitive marketplace. Quality at speed is the customer demand and automation is the biggest bottleneck holding the evolution of quality function. Increased levels of automation and intelligence in software engineering are the emerging trends across the IT field. As systems and software processes guide the life cycle activities and are the vehicles for building quality, it is necessary to look at the process infrastructure for the extent of process automation support provided and the digital enablement. This paper maps out the existing process infrastructure support in industry practice and proposes a roadmap for digital re-imagination of software and systems processes. Harmonizing the quality engineering themes with digital technologies, we propose a framework for building an intelligent software process infrastructure, iSPIN that can help in digital re-imagination of software and systems lifecycle processes. The framework has been implemented using digital technologies and has been piloted with one of the industry business unit for re- imagination of “proposal process”. The proposed iSPIN framework will help in unprecedented automation and quality engineering at each process step and paves the way towards realizing the dictums of “Quality at Speed” and “Digital transformation of Software Process”.

Abstract

Negotiation is an important component of the interaction process among humans. With increasing automation, autonomous agents are expected to take over a lot of this interaction process. Much of automated negotiation literature focuses on agents having a static and known reservation value. In situations involving dynamic environments e.g., an agent negotiating on behalf of a human regarding a meeting, agents can have a reservation value (RV) that is a function of time. This leads to a different set of challenges that may need additional reasoning about the concession behavior. In this paper, we build upon Negotiation algorithms such as ONAC (Optimal Non-Adaptive Concession) and TimeDependent Techniques such as Boulware which work on settings where the reservation value of the agent is fixed and known. Although these algorithms can encode dynamic RV, their concession behavior and hence the properties they were expected to display would be different from when the RV is static, even though the underlying negotiation algorithm remains the same. We, therefore, propose to use one of Counter, Bayesian Learning with Regression Analysis or LSTM model on top of each algorithm to develop the PredictRV strategy and show that PredictRV indeed performs better on two different metrics tested on two different domains on a variety of parameter settings. Keywords: Automated negotiation · Dynamic reservati

Abstract

Memes are steadily taking over the feeds of the public on social media. There is always the threat of malicious users on the internet posting offensive content, even through memes. Hence, the automatic detection of offensive images/memes is imperative along with detection of offensive text. However, this is a much more complex task as it involves both visual cues as well as language understanding and cultural/context knowledge. This paper describes our approach to the task of SemEval-2020 Task 8: Memotion Analysis. We chose to participate only in Task A which dealt with Sentiment Classification, which we formulated as a text classification problem. Through our experiments, we explored multiple training models to evaluate the performance of simple text classification algorithms on the raw text obtained after running OCR on meme images. Our submitted model achieved an accuracy of 72.69% and exceeded the existing baseline’s Macro F1 score by 8% on the official test dataset. Apart from describing our official submission, we shall elucidate how different classification models respond to this task.

Abstract

OCR for printed classical Indic documents written in Sanskrit is a challenging research problem. It involves complexities such as image degradation, lack of datasets and long-length words. Due to these challenges, the word accuracy of available OCR systems, both academic and industrial, is not very high for such documents. To address these shortcomings, we develop a Sanskrit specific OCR system. We present an attention-based LSTM model for reading Sanskrit characters in line images. We introduce a dataset of Sanskrit document images annotated at line level. To augment real data and enable high performance for our OCR, we also generate synthetic data via curated font selection and rendering designed to incorporate crucial glyph substitution rules. Consequently, our OCR achieves a word error rate of 15.97% and a character error rate of 3.71% on challenging Indic document texts and outperforms strong baselines. Overall, our contributions set the stage for application of OCRs on large corpora of classic Sanskrit texts containing arbitrarily long and highly conjoined words.

Abstract

Sentiment-to-sentiment transfer involves changing the sentiment of the given text while preserving the underlying information. In this work, we present a model SentiInc for sentiment-to-sentiment transfer using unpaired mono-sentiment data. Existing sentiment-tosentiment transfer models ignore the valuable sentiment-specific details already present in the text. We address this issue by providing a simple framework for encoding sentiment-specific information in the target sentence while preserving the content information. This is done by incorporating sentiment based loss in the back-translation based style transfer. Extensive experiments over the Yelp dataset show that the SentiInc outperforms state-of-the-art methods by a margin of as large as ∼11% in G-score. The results also demonstrate that our model produces sentiment-accurate and information-preserved sentences.

Abstract

Recent technological advancements in the Internet and Social media usage have resulted in the evolution of faster and efficient platforms of communication. These platforms include visual, textual and speech mediums and have brought a unique social phenomenon called Internet memes. Internet memes are in the form of images with witty, catchy, or sarcastic text descriptions. In this paper, we present a multi-modal sentiment analysis system using deep neural networks combining Computer Vision and Natural Language Processing. Our aim is different than the normal sentiment analysis goal of predicting whether a text expresses positive or negative sentiment; instead, we aim to classify the Internet meme as a positive, negative, or neutral, identify the type of humor expressed and quantify the extent to which a particular effect is being expressed. Our system has been developed using CNN and LSTM and outperformed the baseline score.

Abstract

Off-colour humour is a category of humour which is considered by many to be in poor taste or overly vulgar. Most commonly, off-colour humour contains remarks on particular ethnic group or gender, violence, domestic abuse, acts concerned with sex, excessive swearing or profanity. Blue humour, dark humour and insult humour are types of off-colour humour. Blue and dark humour unlike insult humour are not outrightly insulting in nature but are often misclassified because of the presence of insults and harmful speech. As the primary contributions of this paper we provide an original data-set consisting of nearly 15,000 instances and a novel approach towards resolving the problem of separating dark and blue humour from offensive humour which is essential so that free-speech on the internet is not curtailed. Our experiments show that deep learning methods outperforms other n-grams based approaches like SVM’s, Naive Bayes and Logistic Regression by a large margin.

Abstract

In developing the infrastructure facilities such as irrigation canals and road networks, topography acts as a significant enabler or constraint. Contour maps and low resolution DEMs have been used by Irrigation engineers and planners to assess the canal routing options, which is time consuming and requires repeated evaluations of the potential paths. So, there is a need to develop robust path planning algorithms, including least cost routing, that takes the topographic and engineering constraints while providing potential canal routing paths. Some recent works have attempted to develop algorithms on synthetic data sets but have not been scaled up on high-resolution data sets, limiting their practical use. This work develops a generic algorithm to determine the least-cost flow path between two geo-locations, given the grid-based Digital Elevation Models (DEMs) and a unit cost of construction per length. From the numerous paths that are possible between the two points in any given topography, a distinct least-cost path is identified. The proposed approach is evaluated by computing canal routing paths over publicly available real-world datasets across two different resolutions of 1Km and 90-meter from different sources for Indian terrains. This is then compared against a global reference real-world dataset, Digital Chart of the World (DCW) at 1Km at a grid cell level. The results across multiple stretches shows an average accuracy of 82.09% as measured based on the path overlap against the DCW dataset, proving that the proposed algorithm can be useful in practice.

Abstract

This work presents a highly accurate current reference of 66nA for ultra-low power applications. A very low figureof-merit (FOM) of 1.3501ppm/◦C 2 is achieved on consuming a minimal quiescent current of 199.37nA. To cancel out process variations, the current subtraction technique is employed and a β-multiplier is used to compensate for mobility (µ) and threshold voltage (Vth). In addition, curvature compensation technique backed by PTAT and CTAT current cancellation is adopted to attain a lower temperature coefficient (TC). Hence, an imperceptible variation of accuracy with temperature and supply variations across all process corners is attained. An adopted trimming scheme further minimizes the overall process spread to ±1.515% without compromising on accuracy. Accordingly, a TC of 67.04ppm/◦C over a wide temperature range of -50◦C to 100◦C is obtained. Furthermore, 1.413%/V line sensitivity (LS) in the supply range of 1.38V to 3V is observed. Low power consumption of 275.13nW@1.38V facilitates its use in high-performance low power applications. Index Terms—FOM, curvature compensation, Line Sensitivity, Proportional to Absolute Temperature (PTAT), Complementary to Absolute Temperature (CTAT), ZTC