Sparse Graph Representations for Procedural Instructional Documents

Computation of document similarity is a critical task in various NLP domains that has applications in deduplication, matching, and recommendation. Traditional approaches for document similarity computation include learning representations of documents and employing a similarity or a distance function over the embeddings. However, pairwise similarities and differences are not efficiently captured by individual representations. Graph representations such as Joint Concept Interaction Graph (JCIG) represent a pair of documents as a joint undirected weighted graph. JCIGs facilitate an interpretable representation of document pairs as a graph. However, JCIGs are undirected, and don't consider the sequential flow of sentences in documents. We propose two approaches to model document similarity by representing document pairs as a directed and sparse JCIG that incorporates sequential information. We propose two algorithms inspired by Supergenome Sorting and Hamiltonian Path that replace the undirected edges with directed edges. Our approach also sparsifies the graph to $O(n)$ edges from JCIG's worst case of $O(n^2)$. We show that our sparse directed graph model architecture consisting of a Siamese encoder and GCN achieves comparable results to the baseline on datasets not containing sequential information and beats the baseline by ten points on an instructional documents dataset containing sequential information

Efficient Learning of Decision-Making Models: A Penalty Block Coordinate Descent Algorithm for Data-Driven Inverse Optimization

Decision-making problems are commonly formulated as optimization problems, which are then solved to make optimal decisions. In this work, we consider the inverse problem where we use prior decision data to uncover the underlying decision-making process in the form of a mathematical optimization model. This statistical learning problem is referred to as data-driven inverse optimization. We focus on problems where the underlying decision-making process is modeled as a convex optimization problem whose parameters are unknown. We formulate the inverse optimization problem as a bilevel program and propose an efficient block coordinate descent-based algorithm to solve large problem instances. Numerical experiments on synthetic datasets demonstrate the computational advantage of our method compared to standard commercial solvers. Moreover, the real-world utility of the proposed approach is highlighted through two realistic case studies in which we consider estimating risk preferences and learning local constraint parameters of agents in a multiplayer Nash bargaining game

A Privacy-Preserving Outsourced Data Model in Cloud Environment

Nowadays, more and more machine learning applications, such as medical diagnosis, online fraud detection, email spam filtering, etc., services are provided by cloud computing. The cloud service provider collects the data from the various owners to train or classify the machine learning system in the cloud environment. However, multiple data owners may not entirely rely on the cloud platform that a third party engages. Therefore, data security and privacy problems are among the critical hindrances to using machine learning tools, particularly with multiple data owners. In addition, unauthorized entities can detect the statistical input data and infer the machine learning model parameters. Therefore, a privacy-preserving model is proposed, which protects the privacy of the data without compromising machine learning efficiency. In order to protect the data of data owners, the epsilon-differential privacy is used, and fog nodes are used to address the problem of the lower bandwidth and latency in this proposed scheme. The noise is produced by the epsilon-differential mechanism, which is then added to the data. Moreover, the noise is injected at the data owner site to protect the owners data. Fog nodes collect the noise-added data from the data owners, then shift it to the cloud platform for storage, computation, and performing the classification tasks purposes

Molecular interaction Study in binary mixture of DMSO with formamide and N, N-dimethylformamide

Ultrasonic, volumetric and viscometric investigations have been carried out on DMSO + formamide and DMSO + N, N-dimethylformamide mixtures at three temperatures 293, 303 and 313 K over the entire mole fraction range. From these data, deviation in isentropic compressibility (ΔKs), excess Gibb’s free energy of activation for viscous flow (ΔG*E), excess internal pressure (π) and excess molar enthalpy (H) have been calculated. A Redlich-Kister polynomial equation of third degree has been used to correlate the derived properties of binary liquid mixtures by using the least square method. The observed positive and negative values of excess parameters have been used to study the nature and strength of intermolecular interactions present in these mixtures. Further, theoretical values of ultrasonic velocity have been evaluated using theories and empirical relations

Pattern of hand injuries reported in a tertiary care setting of North India

Background: Hand injuries are very common in this industrialized world. Significant number of patients report every day to the emergency department with various patterns of hand injuries. It is important to understand these patterns in order to plan proper management and develop safety protocols aimed at preventing these injuries.Methods: The present cross sectional study was conducted among 150 patients presenting with Open hand injuries, in the OPD and emergency of Post Graduate Department of Orthopaedics, Government Medical College, Jammu over a period of one year from January 2010 to December 2010.Results: Out of 150 cases 131(87.33%) were males and 19 (12.67%) were females. The commonest age group affected was 21-30 years (34%) followed by 11-20 years (23%). Maximum injuries 90 (60%) occurred in the time interval from 4 pm to midnight. Majority of patients 67 (45%) sustained injury while at work.  Occupation-wise 37 (25%) patients were unskilled workers, mainly laborers, 35 (23%) were farmers, while the remainder belonged to various other professions. In this series machine injuries, assault and road traffic accidents accounted for most injuries, representing 61 (41%), 25 (17%) and 16 (11%) patients respectively. Traumatic amputation (30%) was the most common injury. The index (21%) and middle (21%) fingers were involved more commonly. Tendon injuries (31%) were more frequent than compound fractures (23%).  Conclusions: Hand trauma predominantly affects young males who have occupational exposure to different machines. A proper understanding of the pattern of injury will help in better management

Analysis Of Multimodal Data On Social Media Using Deep Learning Techniques

Contextual text mining known as sentiment analysis identifies and extracts subjective information from the source content. It aids in the detection of sentiments that are good, negative, neutral, etc. It helps companies monitor internet debates in order to learn how the public feels about their brands, goods, and services. However, the only metrics generally utilized in social media stream analysis are straightforward sentiment analysis and count-based metrics. This is analogous to simply scratching the surface and leaving out those priceless discoveries that are just waiting to be made. Sentiment analysis is quickly evolving into a crucial tool to track and comprehend the sentiment in all types of data because people express their thoughts and feelings more freely than ever before. This project's sole objective is to use various latest AI techniques to categorize various sentiments present in audio and text forms into categories like humorous, offensive, and sarcastic. Using datasets with audio files and image files, we trained the model, then we tested it using the test data

An epidemiological study of low back pain in a tertiary care hospital of Jammu, Jammu and Kashmir, India

Background: Backache is a national, personal and clinical problem. It is experienced by most of the population at some time and is a drain on the nation’s resources. Personally, it is distressing because it can remain a major unresolved dilemma and clinically it poses challenges in diagnosis and treatment.Methods: The present cross sectional study was conducted among 200 patients presenting with chronic low back pain, in the OPD of Post Graduate Department of Orthopaedics, Govt. Medical College, Jammu over a period of one year from November 2006 to October 2007.Results: The average age of patients was 38.39 years with slight male predominance. Majority of the patients were non-sedentary workers. In majority of the cases (58%), duration of low backache was from 3 months to 1 year with the average of 25.8 months (2.158 years). The commonest mode of presentation was low back pain with radiation to lower limbs. Seasonal variation in the intensity of pain was observed in 50% of the cases. Tenderness of the spine was the commonest physical sign. Disc degenerative disease was found to be commonest cause of low backache, being present in 72% of the cases.Conclusions: Low back pain is common in 3rd and 4th decade of life. The commonest mode of presentation was low back pain with radiation to lower limbs. Disc degenerative disease was found to be commonest cause of low backache, being present in 72% of the cases