Abstract

A system and method for automatically extracting contract data from electronic contracts includes an administrator module configured to provide templates for inputting document patterns and a list of contract data tags for each of a plurality of contract document types. A parser is configured to convert an electronic contract document into a contract text document and reformat the contract text document to provide a pattern for the text contract document. A pattern recognition engine is configured to determine a list of contract document types in the electronic contract by comparing and matching patterns of all known contract document types with the pattern of the contract text document. A contract data extraction engine is configured to extract contract data for each contract document type on the list.

Abstract

The relocation of cinema away from public screenings to the viewership afforded by Netflix has reterritorialized a new form of spectatorship, allowing filmmakers to tell stories otherwise deemed too risky/risqué and attempt to re/claim a language of social and political critique. All productions circulate in and acknowledge new globalities, changed sexual and power relations of image cultures and the traction afforded to a deterritorialized screening space. However, they are bound by the exigencies of protecting a particularly middle-class urban diasporic spectator from the phantom of the mobilisation of a class and nurturing a fantasy of individual mobility. This paper will look at two recent productions screened via Netflix – Sir (Rohena Gera, 2018) and The White Tiger (Ramin Bahrani, 2021) to interrogate specificities of the interpretive community created by an OTT platform in a country like India. Both texts interrogate social anatomies of desire in a class and caste based society. Yet, any oppositional gaze is necessarily bound by the conditions of distribution and reception specific to Netflix. This paper shall demonstrate that formal aspects of the aesthetics of these films simultaneously produce and preclude a critical space.

Abstract

This Full Paper in the Innovative Practice category begins by asking “can algorithms be thought of and taught as dynamical systems?” Our exploration of this idea — Algodynamics — is guided by a vision to achieve convergence between computing and engineering education. The engineering sciences share a common conceptual vocabulary originating in dynamical systems: state spaces, flows, actions, invariants, fixed points, convergence, etc. The goal of algodynamics is to build, ab initio, a framework for understanding and teaching algorithms using concepts from dynamics. This allows us to teach computing and algorithms as an engineering science. Engineers work with models. In algodynamics, models are expressed using transition systems rather than as pseudocode or programs. This allows a crisp representation of two important classes of computation: sequential algorithms as ‘discrete flows’ (iterative systems) and interactive applications as ‘action flows’ (transition systems).

Abstract

Systemic lupus erythematosus (SLE) is an autoimmune disease in which the immune system of the patient starts attacking healthy tissues of the body. Lupus Nephritis (LN) refers to the inflammation of kidney tissues resulting in renal failure due to these attacks. The International Society of Nephrology/Renal Pathology Society (ISN/RPS) has released a classification system based on various patterns observed during renal injury in SLE. Traditional methods require meticulous pathological assessment of the renal biopsy and are time-consuming. Recently, computational techniques have helped to alleviate this issue by using virtual microscopy or Whole Slide Imaging (WSI). With the use of deep learning and modern computer vision techniques, we propose a pipeline that is able to automate the process of 1) detection of various glomeruli patterns present in these whole slide images and 2) classification of each image using the extracted glomeruli features.

Abstract

Well-defined jokes can be divided neatly into a setup and a punchline. While most works on humor today talk about a joke as a whole, the idea of generating punchlines to a setup has applications in conversational humor, where funny remarks usually occur with a non-funny context. Thus, this paper is based around two core concepts: Classification and the Generation of a punchline from a particular setup based on the Incongruity Theory. We first implement a feature-based machine learning model to classify humor. For humor generation, we use a neural model, and then merge the classical rule-based approaches with the neural approach to create a hybrid model. The idea behind being: combining insights gained from other tasks with the setup-punchline model and thus applying it to existing text generation approaches. We then use and compare our model with human written jokes with the help of human evaluators in a double-blind study.

Abstract

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Abstract

In this paper, we investigate the problem of text coherence modeling with a new perspective of learning better distributed sentence representation by incorporating document dependent and document independent features of a sentence. To this end, we propose a datadriven end-to-end novel neural coherence model that captures text coherence by exploiting the semantic and distributional aspects of the sentences in a document. The network is able to capture various document independent and document dependent features of a sentence which is imperative in assessing text coherence. Experiments on the standard Sentence Ordering task indicate that our proposed model shows significant performance gain of 1.7 percent in terms of accuracy score compared with the state-of-the-art baselines.

Abstract

Normalizing flows are an essential alternative to GANs for generative modelling, which can be optimized directly on the maximum likelihood of the dataset. They also allow computation of the exact latent vector corresponding to an image since they are composed of invertible transformations. However, the requirement of invertibility of the transformation prevents standard and expressive neural network models such as CNNs from being directly used. Emergent convolutions were proposed to construct an invertible 3×3 CNN layer using a pair of masked CNN layers, making them inefficient. We study conditions such that 3×3 CNNs are invertible, allowing them to construct expressive normalizing flows. We derive necessary and sufficient conditions on a padded CNN for it to be invertible. Our conditions for invertibility are simple, can easily be maintained during the training process. Since we require only a single CNN layer for every effective invertible CNN layer, our approach is more efficient than emerging convolutions. We also proposed a coupling method, Quad-coupling. We benchmark our approach and show similar performance results to emergent convolutions while improving the model's efficiency

Abstract

Artificial Bee Colony (ABC)method optimization procedure for design of RC moment frames​. Cross-section properties of frame members are optimized considering constraints specified by design codes.​Structural design provisions enunciated in Indian Standards IS456 (2000), IS1893 (2016) and IS13920 (2016) are used as system of design rules, to interact with Finite Element Method of analysis of frames developed for the study. Cross-sectional properties of structural members are chosen from a set of pre-defined sectional properties from the database developed to form design variables compliant with Indian Standards. The optimized design & detailing of frame elements are obtained after the frame structure is analyzed under the action of multiple load combinations ​ ABC method can then be applied, to improve the Nonlinear Response of the frame​

Abstract

The advent of Internet of Things has resulted in new communication protocols such as LoRaWAN and Narrowband IoT slowly rising to be viable and even better alternatives to traditional protocols such as Wi-Fi and Bluetooth. LoRa in particular, has attracted a lot of attention owing to low power consumption of end nodes, low cost and use of license-free sub-GHz bands. Further, high link budgets in LoRaWAN communication allow for fewer gateways to provide network coverage over a large area. However, the success of deployment of LoRaWAN gateways in real world applications depends on meticulous planning. A major drawback in this scenario is the lack of extensive data on loss of signal strength, particularly in cityscapes, where buildings and trees can create significant path loss. In this paper, we investigate the path loss for LoRaWAN signals inside a typical city building (brick walls, reinforced cement concrete ceiling, wooden doors). The RSSI values of transmissions from multiple locations to a stationary gateway have been analysed to find the effect of indoor obstacles such as walls and doors on the propagation of LoRaWAN signals. We report that significant loss of energy can occur, even at relatively close range, because of the structures.