Influence of Demographic Variables on Consumer Ethnocentrism: Case of Rajasthan, India

This research will attempt to explore and analyse the belongings of several demographic variables i.e. gender, age, education level, income level, and nature of dwelling on the consumer ethnocentric tendencies among consumers of Rajasthan. Five independent variables gender, age, education level, income level, and nature of dwelling are taken to check the ethnocentric behaviour of respondents towards buying Fast Moving Consumer Goods. Respondents were approached from Jaipur, Rajasthan and 5-point Likert was used to measure the variables. SPSS version 21 was used for data analyses to instigate with the demographic profile of the respondents. The investigation of the differences between subgroups in the demographic variables was tested using independent sample T-test and one-way ANOVA. From results it was concluded that Indians above 31 years were more ethnocentric then younger generation and no significant relation was found between other demographic variables and ethnocentric tendencies of the consumer of Rajasthan. Keywords: Consumer Ethnocentrism, Demographic characteristics – age, gender, income level, education level, Nature of dwelling DOI: 10.7176/JMCR/55-01 Publication date: April 30th 201

The Impact of the Ongoing Pandemic on Digital Finance Transactions: An Empirical Analysis

The ongoing pandemic has resulted in a disruption of the life of all citizens and impacted all the spheres, more so the financial system because the Pandemic and its aftermath has shut all economic activity except those which as per the government directives are considered the most essential. This has deeply impacted private consumption, external trade as well as investment in the economy. Accordingly, both in retail stores and e-commerce orders, a common strand is that many of the consumers are now paying bills via digital payment mechanisms and taking contactless delivery of goods wherever possible. “Digital financial transaction systems, e-wallets and apps, online transactions using e-banking, usage of Plastic money (Debit and Credit Cards), etc. have recorded a substantial increase in demand during the crisis”. The objective of the present paper is to examine and analyze the digital finance transactions in selected cities during the ongoing pandemi

A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs

Cyber security is one of the most significant technical challenges in current times. Detecting adversarial activities, prevention of theft of intellectual properties and customer data is a high priority for corporations and government agencies around the world. Cyber defenders need to analyze massive-scale, high-resolution network flows to identify, categorize, and mitigate attacks involving networks spanning institutional and national boundaries. Many of the cyber attacks can be described as subgraph patterns, with prominent examples being insider infiltrations (path queries), denial of service (parallel paths) and malicious spreads (tree queries). This motivates us to explore subgraph matching on streaming graphs in a continuous setting. The novelty of our work lies in using the subgraph distributional statistics collected from the streaming graph to determine the query processing strategy. We introduce a "Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named "Relative Selectivity" that is used to select between different query processing strategies. Our experiments performed on real online news, network traffic stream and a synthetic social network benchmark demonstrate 10-100x speedups over selectivity agnostic approaches.Comment: in 18th International Conference on Extending Database Technology (EDBT) (2015

NOUS: Construction and Querying of Dynamic Knowledge Graphs

The ability to construct domain specific knowledge graphs (KG) and perform question-answering or hypothesis generation is a transformative capability. Despite their value, automated construction of knowledge graphs remains an expensive technical challenge that is beyond the reach for most enterprises and academic institutions. We propose an end-to-end framework for developing custom knowledge graph driven analytics for arbitrary application domains. The uniqueness of our system lies A) in its combination of curated KGs along with knowledge extracted from unstructured text, B) support for advanced trending and explanatory questions on a dynamic KG, and C) the ability to answer queries where the answer is embedded across multiple data sources.Comment: Codebase: https://github.com/streaming-graphs/NOU

Successful pregnancy outcome in a patient of chronic myeloid leukemia on imatinib therapy

Pregnancy and cancer is a complex situation. The coincidence chronic myeloid leukemia (CML) and pregnancy is an uncommon event, in part because CML occurs mostly in older age group. The management of CML, during pregnancy is a difficult problem because of potential effects of therapy on the mother and foetus. Imatinib, a tyrosine kinase inhibitor induces dramatic hematologic and cytogenetic responses in CML, but it is not recommended for use in pregnancy or if the patient plans to conceive. In literature there are very few reports of successful outcome of pregnancy while on imatinib. In this report we describe a successful pregnancy and labor under treatment with imatinib in a known case of CML.

Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes

Question Answering (QA) in clinical notes has gained a lot of attention in the past few years. Existing machine reading comprehension approaches in clinical domain can only handle questions about a single block of clinical texts and fail to retrieve information about multiple patients and their clinical notes. To handle more complex questions, we aim at creating knowledge base from clinical notes to link different patients and clinical notes, and performing knowledge base question answering (KBQA). Based on the expert annotations available in the n2c2 dataset, we first created the ClinicalKBQA dataset that includes around 9K QA pairs and covers questions about seven medical topics using more than 300 question templates. Then, we investigated an attention-based aspect reasoning (AAR) method for KBQA and analyzed the impact of different aspects of answers (e.g., entity, type, path, and context) for prediction. The AAR method achieves better performance due to the well-designed encoder and attention mechanism. From our experiments, we find that both aspects, type and path, enable the model to identify answers satisfying the general conditions and produce lower precision and higher recall. On the other hand, the aspects, entity and context, limit the answers by node-specific information and lead to higher precision and lower recall.Comment: Accepted to ACM BCB 202

A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks

Hyperbolic neural networks are able to capture the inherent hierarchy of graph datasets, and consequently a powerful choice of GNNs. However, they entangle multiple incongruent (gyro-)vector spaces within a layer, which makes them limited in terms of generalization and scalability. In this work, we propose to use Poincar\'e disk model as our search space, and apply all approximations on the disk (as if the disk is a tangent space derived from the origin), and thus getting rid of all inter-space transformations. Such an approach enables us to propose a hyperbolic normalization layer, and to further simplify the entire hyperbolic model to a Euclidean model cascaded with our hyperbolic normalization layer. We applied our proposed nonlinear hyperbolic normalization to the current state-of-the-art homogeneous and multi-relational graph networks. We demonstrate that not only does the model leverage the power of Euclidean networks such as interpretability and efficient execution of various model components, but also it outperforms both Euclidean and hyperbolic counterparts in our benchmarks