Abstract

Generating domain-specific content such as legal clauses based on minimal user-provided information can be of significant benefit in automating legal contract generation. In this paper, we propose a controllable graph-based mechanism that can generate legal clauses using only the topic or type of the legal clauses. Our pipeline consists of two stages involving a graph-based planner followed by a clause generator. The planner outlines the content of a legal clause as a sequence of keywords in the order of generic to more specific clause information based on the input topic using a controllable graph-based mechanism. The generation stage takes in a given plan and generates a clause. The pipeline consists of a graph-based planner followed by text generation. We illustrate the effectiveness of our proposed two-stage approach on a broad set of clause topics in contracts.

Abstract

Premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD) are medically unexplained disorders that occur with somatic or psychiatric symptoms presenting during the luteal phase of the menstrual cycle, ending within a few days after the onset of menstruation. Worldwide, the prevalence of PMS is remarkably high, and its causes are still unclear and are multifactorial. This chapter aims to understand better the etiopathogenesis, clinical features, diagnosis, contemporary, and integrative holistic approaches of PMS management. A thorough literature survey from various scientific databases such as Web of Science, ScienceDirect, PubMed, Google Scholar, Scopus, and other databases was retrieved for the

Abstract

Source code is usually accompanied by comments which help in improving code comprehension. However, not all comments are helpful in this respect, with some comments being redundant, some being unclear which results in a poorer code readability. Sifting through large volumes of code to identify such comments manually being tedious, the task of automatically evaluating the usefulness of a comment in context of the code it lies in can add value. In this work, we evaluate the performance of various pretrained transformer encoders to solve this task. We demonstrate decent performance of a few models alongside abnormally high performance obtained by a few other pretrained architectures

Abstract

The sudden shift to online education due to the pandemic has led to widespread learning gaps in the student population, increasing the need for individualistic attention to student learning as we transition back to physical classrooms. One can take advantage of the advancements in technology to help in this process by developing tools that can assist teachers decrease the amount of administrative workload and improve their ability to track the learning and understanding of every student in the classroom. The current work proposes a framework for accurate, low-cost, and scalable computer visionbased attendance and attention tracking in physical classroom environment to tackle this problem

Abstract

Deployment of Accident Relief Medical Vans (ARMV) used for accident relief and rescue operations is generally done heuristically. ARMV is an ambulance-train equipped with various tools like jacks, heavy cutting tools, generators, operation theatre, artificial breathing equipment etc. to ease the rescue process. Taking North Eastern Railway (NER) as a case study, Location Set Covering Problem (LSCP) model will be used to find the minimum number of ARMVs required for the given network and their locations so that all demand points can be ‘covered’ within a maximum of one hour @ 100 kmph for each of the nearest facility. This paper proposes a new mathematical model based on the Cluster Coverage Model (CCM) to provide a better coverage of ARMV’s demands. We have used Girvan-Newman (GN) algorithm, a weight-based graph for clustering of NER network where nodes are taken as depots with ARMV parking facilities & crew availability and distance between these nodes is taken as edge weight of the graph. Finally, we present a comparison of results between the proposed cluster coverage models with LSCP and suggest relocations of the existing ARMVs

Abstract

In this study, we show that DNA methylation markers can be used for the early detection of breast cancer subtypes based on the status of Estrogen, Progesterone and HER2 receptors. A machine learning approach is used to predict breast cancer patients’ receptor status. DNA methylation datasets created using Illumina Hypermethylation 450K platform from four different studies from NCBI GEO and TCGA-BRCA are collated to create a dataset of 1514 samples. Of these, 1260 samples passed quality control and had receptor status information available, and these 1260 samples were used for the training and testing of the SVM model. We analysed the prognostic value of DNA methylation-based receptor status prediction by comparing the survival outcomes with the traditional methods. We observe that patients whose classification status does not match with those of traditional methods exhibit lower survival probability. This could possibly be due to incorrect classification and treatment plans by the traditional IHC method. This suggests that

Abstract

Customer support is an essential part of any company to ensure customer satisfaction and business growth. The responsibility and scale get even higher for enterprise companies like Freshworks that build customer support platforms for its enterprise customers from multiple and previously unseen domains. A ticket may carry issues never seen before, therefore static, and predefined problem labels will not always work. These predefined problem labels are often created manually by admins. For a large customer account, roughly 10-35% of the problem labels could get discovered by this manual process and only 5-9% of the available open tickets could get linked to a problem label. So, there is a need for a system for a new incoming ticket. The system should either automatically detect an existing problem topic with its possible suggestions or systematically generate new problem topic(s) for its assignment. Our system is based on an incremental approach to mine ticket data streams and assigns them to hierarchical soft clusters with auto-generated problem/issue topics. Our system achieves an improvement in ticket assignments up to 97%. To compare with recent clustering advancements, we’ve also evaluated our approach on a public dataset [9] and achieved comparable results. Source code and results of the evaluation are available in detail at: https://github.com/vinidixit/hierarchical-labelled-clustering-evaluation.

Abstract

We derive a formula for p(n) (the number of partitions of n) in terms of the partial Bell polynomials using Faà di Bruno’s formula and Euler’s pentagonal number theorem.

Abstract

Few animals can hide their signatures into their surroundings to hunt or avoid being hunted, termed as natural camouflage. Natural camouflage inspires researchers to design various algorithms to artificially create camouflage images and widely apply them in the military field. It is also essential to evaluate the effectiveness of artificially created camouflage images. Several algorithms have been developed in the literature to evaluate camouflage design techniques. Camouflage breaking systems extract the object from its surroundings. Due to the complexity of the problem, it is challenging to design an algorithm for breaking camouflage.

Abstract

Grain segmentation of sandstone that is partitioning the grain from its surrounding matrix/cement in the thin section is the primary step for computer-aided mineral identification and sandstone classification. The microscopic images of sandstone contain many mineral grains and their surrounding matrix/cement. The distinction between adjacent grains and the matrix is often ambiguous, making grain segmentation difficult. Various solutions exist in literature to handle these problems; however, they are not robust against sandstone petrography's varied pattern. In this paper, we formulate grain segmentation as a pixel-wise two-class (i.e., grain and background) semantic segmentation task. We develop a deep learning-based end-to-end trainable framework named Deep Semantic Grain Segmentation network (DSGSN), a data-driven method, and provide a generic solution. As per the authors' knowledge, this is the first work where the deep neural network is explored to solve the grain segmentation problem. Extensive experiments on microscopic images highlight that our method obtains better segmentation accuracy than various segmentation architectures with more parameters.