Abstract

Decay heat removal(DHR) system plays a crucial role in maintaining the reactor safety with the ultimate cooling arrangement in case all the other intended systems are unavailable. As a special case, the system shall also ensure that the reactor is not overcooled so that the coolant is not frozen. In this regard, a passive device is to be developed to meet the requirement of monitoring DHR system temperature. Toward this, a self-sustained instrument has been designed and tested with all the necessary compensation and calibration for the entire reactor life. This paper explains the design of such a sensor and its future scope.

Abstract

Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using leaderboards, but the evaluation choices become sub-optimal as better alternatives arise. This problem is especially pertinent in natural language generation which requires everimproving suites of datasets, metrics, and human evaluation to make definitive claims. To make following best model evaluation practices easier, we introduce GEMv2. The new version of the Generation, Evaluation, and Metrics Benchmark introduces a modular infrastructure for dataset, model, and metric developers to benefit from each others work. GEMv2 supports 40 documented datasets in 51 languages. Models for all datasets can be evaluated online and our interactive data card creation and rendering tools make it easier to add new datasets to the living benchmark.

Abstract

Non-Fungible Tokens (NFTs) are a relatively unexplored class of assets. Designing strategies to forecast NFT trends is an intricate task due to its extremely volatile nature. The market is largely driven by public sentiment and "hype", which in turn has a high correlation with conversations taking place on social media platforms like Twitter. Prior work done for modelling stock market data does not take into account the extent of impact certain highly influential tweets and their authors can have on the market. Building on these limitations and the nature of the NFT market, we propose a novel reachaware temporal learning approach to make predictions for forecasting future trends in the NFT market. We perform experiments on a new dataset consisting of over 1.3 million tweets and 180 thousand NFT transactions spanning over 15 NFT collections curated by us. Our model (TA-NFT) outperforms other state-ofthe-art methods by an average of 36%. Through extensive quantitative and ablative analysis, we demonstrate the ability of our approach as a practical method for predicting NFT trends.

Abstract

The COVID-19 pandemic required efficient allocation of public resources and transforming existing ways of societal functions. To manage any crisis, governments and public health researchers ex-ploit the information available to them in order to make informed decisions, also defined as situational awareness. Gathering situational awareness using so-cial media, has been functional to manage epidemics. Previous research focused on using discussions during periods of epidemic crises on social media platforms like Twitter, Reddit, or Facebook and developing NLP techniques to filter out important/relevant discussions from a huge corpus of messages and posts. Social media usage varies with internet penetration and other socio-economic factors, which might induce disparity in an-alyzing discussions across different geographies. How-ever, print media is a ubiquitous information source, irrespective of geography. Further, topics discussed in news articles are already ‘newsworthy’, while on social media ‘newsworthiness' is a product of techno-social processes. Developing this fundamental difference, we study Twitter data during the second wave in India focused on six high-population cities with varied macro-economic factors. Through a mixture of qualitative and quantitative methods, we further analyze two Indian newspapers during the same period

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