Quantum computing is entering the mainstream as part of cloud offerings, where it serves as a special-purpose accelerator in larger applications. However, it is still challenging for developers and researchers to design, build and manage the resources for Hybrid Quantum-Classical (HQC) applications that include both classical logic (x86, ARM) and quantum circuit blocks, and run across both traditional and quantum processors. Further, quantum hardware is available on public cloud and even on-premise as private clouds, with varying capabilities, costs and queue times. Further, such quantum circuits also expose optimization methods that offer cost, time, accuracy and parallelism trade-offs. So, there is a compelling for easy composition of HQC applications that can be effortlessly and efficiently deployed on hybrid clouds. In this paper, we propose a framework to intuitively compose and deploy “zero-touch” quantum-classical Function-as-a-Service (FaaS) workflows through various workflow patterns that leverage diverse cloud system (workflow partitioning, adaptive polling) and quantum (circuit cutting, qubit reuse) optimizations. These utilize our XFaaS FaaS workflow framework for hybrid cloud deployments on AWS and Azure, and IBM Qiskit SDK for the quantum circuit toolchain. We also offer detailed experimental profiling of these optimizations for realistic and synthetic HQC applications on real clouds, and on real and simulated quantum hardware, and analyze the benefits of cloud system and quantum circuit optimizations. Our results demonstrate up to 53 % improvement in time and 80 % in cost when quantum circuit optimization on hardware is used in conjunction with dynamic fan-out.

Sarcasm is a complex linguistic construct with incongruity at its very core.Detecting sarcasm depends on the actual content spoken and tonality, facial expressions, the context of an utterance, and personal traits like language proficiency and cognitive capabilities.In this paper, we propose the utilization of synthetic gaze data to improve the task performance for multimodal sarcasm detection in a conversational setting.We enrich an existing multimodal conversational dataset, i.e., MUStARD++ with gaze features.With the help of human participants, we collect gaze features for < 20% of data instances, and we investigate various methods for gaze feature prediction for the rest of the dataset.We perform extrinsic and intrinsic evaluations to assess the quality of the predicted gaze features.We observe a performance gain of up to 6.6% points by adding a new modality, i.e., collected gaze features.When both collected and predicted data are used, we observe a performance gain of 2.3% points on the complete dataset.Interestingly, with only predicted gaze features, too, we observe a gain in performance (1.9% points).We retain and use the feature prediction model, which maximally correlates with collected gaze features.Our model trained on combining collected and synthetic gaze data achieves SoTA performance on the MUStARD++ dataset.To the best of our knowledge, ours is the first predict-and-use model for sarcasm detection.We publicly release 1 the code, gaze data, and our best models for further research.

While sentiment and emotion analysis have been studied extensively, the relationship between sarcasm and emotion has largely remained unexplored. A sarcastic expression may have a variety of underlying emotions. For example, “I love being ignored” belies sadness, while “my mobile is fabulous with a battery backup of only 15 minutes!” expresses frustration. Detecting the emotion behind a sarcastic expression is non-trivial yet an important task. We undertake the task of detecting the emotion in a sarcastic statement, which to the best of our knowledge, is hitherto unexplored. We start with the recently released multimodal sarcasm detection dataset (MUStARD) pre-annotated with 9 emotions. We identify and correct 343 incorrect emotion labels (out of 690). We double the size of the dataset, label it with emotions along with valence and arousal which are important indicators of emotional intensity. Finally, we label each sarcastic utterance with one of the four sarcasm types-Propositional, Embedded, Likeprefixed and Illocutionary, with the goal of advancing sarcasm detection research. Exhaustive experimentation with multimodal (text, audio, and video) fusion models establishes a benchmark for exact emotion recognition in sarcasm and outperforms the state-of-art sarcasm detection. We release the dataset enriched with various annotations and the code for research purposes: https://github.com/apoorva-nunna/MUStARD_Plus_Plus

Technical support domain involves solving problems from user queries through various channels: voice, web and chat, and is both time-consuming and labour intensive. The textual queries in web or chat mode are unstructured and often incomplete. This affects information retrieval and increases the difficulty level for agents to solve it. Such cases require multiple rounds of interaction between user and agent/chatbot in order to better understand the user query. This paper presents a deployed system called Question Quality Improvement (QQI), that aims to improve the quality of user utterance by understanding and extracting important parts of an utterance and gamifying the user interface, prompting them to enter the remaining relevant information. QQI is guided by an ontology designed for the technical support domain and uses co-reference resolution and deep parsing to understand the sentences. Using the syntactics and semantics in the deep parse tree structure various attributes in the ontology are extracted. The system has been in production for over two years supporting around 800 products resulting in a reduction in the time-to-resolve cases by around 29%, leading to huge cost savings. QQI being a core natural language understanding and metadata extraction technology, directly affects more than 8K tickets everyday. These cases are submitted after 50K edits done on the case based on QQI feedback. QQI outputs are used by other technologies such as search and retrieval, case routing for automated dispatch, case-difficulty-prediction, and by the chatbots supported in each product page.

In this paper, we propose a novel attention based coupled framework for road and pothole segmentation. In many developing countries as well as in rural areas, the drivable areas are neither well-defined, nor well-maintained. Under such circumstances, an Advance Driver Assistant System (ADAS) is needed to assess the drivable area and alert about the potholes ahead to ensure vehicle safety. Moreover, this information can also be used in structured environments for assessment and maintenance of road health. We demonstrate few-shot learning approach for pothole detection to leverage accuracy even with fewer training samples. We report the exhaustive experimental results for road segmentation on KITTI and IDD datasets. We also present pothole segmentation on IDD.

Ticket assignment/dispatch is a crucial part of service delivery business with lot of scope for automation and optimization. In this paper, we present an end-to-end automated helpdesk email ticket assignment system, which is also offered as a service. The objective of the system is to determine the nature of the problem mentioned in an incoming email ticket and then automatically dispatch it to an appropriate resolver group (or team) for resolution.The proposed system uses an ensemble classifier augmented with a configurable rule engine. While design of a classifier that is accurate is one of the main challenges, we also need to address the need of designing a system that is robust and adaptive to changing business needs. We discuss some of the main design challenges associated with email ticket assignment automation and how we solve them. The design decisions for our system are driven by high accuracy, coverage, business continuity, scalability and optimal usage of computational resources.Our system has been deployed in production of three major service providers and currently assigning over 90,000 emails per month, on an average, with an accuracy close to 90% and covering at least 90% of email tickets. This translates to achieving human-level accuracy and results in a net saving of more than 50000 man-hours of effort per annum. Till date, our deployed system has already served more than 700,000 tickets in production.

Recognizing text from degraded and low-resolution document images is still an open challenge in the vision community. Existing text recognition systems require a certain resolution and fails if the document is of low-resolution or heavily degraded or noisy. This paper presents an end-to-end trainable deep-learning based framework for joint optimization of document enhancement and recognition. We are using a generative adversarial network (GAN) based framework to perform image denoising followed by deep back projection network (DBPN) for super-resolution and use these super-resolved features to train a bidirectional long short term memory (BLSTM) with Connectionist Temporal Classification (CTC) for recognition of textual sequences. The entire network is end-to-end trainable and we obtain improved results than state-of-the-art for both the image enhancement and document recognition tasks. We demonstrate results on both printed and handwritten degraded document datasets to show the generalization capability of our proposed robust framework.

Technical support problems are very complex. In contrast to regular web queries (that contain few keywords) or factoid questions (which are a few sentences), these problems usually include attributes like a detailed description of what is failing (symptom), steps taken in an effort to remediate the failure (activity), and sometimes a specific request or ask (intent). Automating support is the task of automatically providing answers to these problems given a corpus of solution documents. Traditional approaches to this task rely on information retrieval and are keyword based; looking for keyword overlap between the question and solution documents and ignoring these attributes. We present an approach for semantic parsing of technical questions that uses grammatical structure to extract these attributes as a baseline, and a CRF based model that can improve performance considerably in the presence of annotated data for training. We also demonstrate that combined with reasoning, these attributes help outperform retrieval baselines.

Recognizing text from noisy low-resolution (LR) images is extremely challenging and is an open problem for the computer vision community. Super-resolving a noisy LR text image results in noisy High Resolution (HR) text image, as super-resolution (SR) leads to spatial correlation in the noise, and further cannot be de-noised successfully. Traditional noise-resilient text image super-resolution methods utilize a denoising algorithm prior to text SR but denoising process leads to loss of some high frequency details, and the output HR image has missing information (texture details and edges). This paper proposes a noise-resilient SR framework for text images and recognizes the text using a deep BLSTM network trained on high resolution images. The proposed end-to-end deep learning based framework for noise-resilient text image SR simultaneously perform image denoising and super-resolution as well as preserves missing details. Stacked sparse denoising auto-encoder (SSDA) is learned for LR text image denoising, and our proposed coupled deep convolutional auto-encoder (CDCA) is learned for text image super-resolution. The pretrained weights for both these networks serve as initial weights to the end-to-end framework during finetuning, and the network is jointly optimized for both the tasks. We tested on several Indian Language datasets and the OCR performance of the noise-resilient super-resolved images is at par with the original HR images.

With the rapid increase of multimedia data, textual content in an image has become a very important source of information for several applications like navigation, image search and retrieval, image understanding, captioning, machine translation and several others. Scene text localization is the first step towards such applications and most current methods focus on generating a small set of high precision detectors rather than obtaining large set of detections covering all text patches. In this work we propose a novel hybrid framework for text localization which uses character level recognition recursively in a feedback mechanism to refine text patches and reduce false positives. We use popular MSER algorithm at multiple scales as an initial region proposal algorithm and several filtering stages recursively to improve precision as well as maximize recall. We aim at achieving high recall rather than achieving higher precision since several robust word recognition systems are already available. The word recognition systems are mature enough to produce highly accurate results if provided with maximum amount of regions rather than providing small set of highly precise text patches and losing several other text regions. The main contribution of this paper is the use of character recognizer within a novel feedback mechanism to recursively search for text regions in the neighborhood of previously detected text patches. Using 3 publicly available benchmark datasets (ICDAR2011, MSRA TD-500 and OSTD), we demonstrate the efficacy of the proposed framework for text localization.

Deep LSTM is an ideal candidate for text recognition. However text recognition involves some initial image processing steps like segmentation of lines and words which can induce error to the recognition system. Without segmentation, learning very long range context is difficult and becomes computationally intractable. Therefore, alternative soft decisions are needed at the pre-processing level. This paper proposes a hybrid text recognizer using a deep recurrent neural network with multiple layers of abstraction and long range context along with a language model to verify the performance of the deep neural network. In this paper we construct a multi-hypotheses tree architecture with candidate segments of line sequences from different segmentation algorithms at its different branches. The deep neural network is trained on perfectly segmented data and tests each of the candidate segments, generating unicode sequences. In the verification step, these unicode sequences are validated using a sub-string match with the language model and best first search is used to find the best possible combination of alternative hypothesis from the tree structure. Thus the verification framework using language models eliminates wrong segmentation outputs and filters recognition errors.

Script based features are highly discriminative for text segmentation and recognition. Thus they are widely used in Optical Character Recognition(OCR) problems. But usage of script dependent features restricts the adaptation of such architectures directly for another script. With script independent systems, this problem can be solved to a certain extent for monolingual documents. But the problem aggravates in case of multilingual documents as it is very difficult for a single classifier to learn many scripts. Generally a script identification module identifies text segments and accordingly the script-dependent classifier is selected. This paper presents a unified framework of language model and multiple preprocessing hypotheses for word recognition from bilingual document images. Prior to text recognition, preprocessing steps such as binarization and segmentation are required for ease of recognition. But these steps induce huge combinatorial error propagating to final recognition accuracy. In this paper we use multiple preprocessing routines as alternate hypotheses and use a language model to verify each alternative and choose the best recognized sequence. We test this architecture for word recognition of Kannada-English and Telugu-English bilingual documents and achieved better recognition rates than single methods using same classifier.

The paper presents a novel script independent CRF based inferencing framework for character recognition. In this framework we consider a word as a sequence of connected components. The connected components are obtained using different binarization schemes and different possible sequences are considered using a tree structure. CRF uses contextual information to learn perfect primitive sequences and finds the most probable labeling of the sequence of primitives using multiple hypothesis tree to form the correct sequence of alphabets. This approach is particularly suitable for degraded printed document images as it considers multiple alternate hypotheses for correct decision.