392 research outputs found
Top K Relevant Passage Retrieval for Biomedical Question Answering
Question answering is a task that answers factoid questions using a large
collection of documents. It aims to provide precise answers in response to the
user's questions in natural language. Question answering relies on efficient
passage retrieval to select candidate contexts, where traditional sparse vector
space models, such as TF-IDF or BM25, are the de facto method. On the web,
there is no single article that could provide all the possible answers
available on the internet to the question of the problem asked by the user. The
existing Dense Passage Retrieval model has been trained on Wikipedia dump from
Dec. 20, 2018, as the source documents for answering questions. Question
answering (QA) has made big strides with several open-domain and machine
comprehension systems built using large-scale annotated datasets. However, in
the clinical domain, this problem remains relatively unexplored. According to
multiple surveys, Biomedical Questions cannot be answered correctly from
Wikipedia Articles. In this work, we work on the existing DPR framework for the
biomedical domain and retrieve answers from the Pubmed articles which is a
reliable source to answer medical questions. When evaluated on a BioASQ QA
dataset, our fine-tuned dense retriever results in a 0.81 F1 score.Comment: 6 pages, 5 figures. arXiv admin note: text overlap with
arXiv:2004.04906 by other author
An Ethereum-based Product Identification System for Anti-counterfeits
Fake products are items that are marketed and sold as genuine, high-quality
products but are counterfeit or low-quality knockoffs. These products are often
designed to closely mimic the appearance and branding of the genuine product to
deceive consumers into thinking they are purchasing the real thing. Fake
products can range from clothing and accessories to electronics and other goods
and can be found in a variety of settings, including online marketplaces and
brick-and-mortar stores. Blockchain technology can be used to help detect fake
products in a few different ways. One of the most common ways is through the
use of smart contracts, which are self-executing contracts with the terms of
the agreement between buyer and seller being directly written into lines of
code. This allows for a high level of transparency and traceability in supply
chain transactions, making it easier to identify and prevent the sale of fake
products and the use of unique product identifiers, such as serial numbers or
QR codes, that are recorded on the blockchain. This allows consumers to easily
verify the authenticity of a product by scanning the code and checking it
against the information recorded on the blockchain. In this study, we will use
smart contracts to detect fake products and will evaluate based on Gas cost and
ethers used for each implementation.Comment: 5 page, 5 figure
CSSXC: Context-sensitive Sanitization Framework for Web Applications against XSS Vulnerabilities in Cloud Environments
AbstractThis paper presents a context-sensitive sanitization based XSS defensive framework for the cloud environment. It discovers all the hidden injection points in HTML5-based web applications deployed on the platforms of cloud and sanitizes the XSS attack payloads injected in such points in a context sensitive manner. The identification of such injection points permits our technique to retrieve each possible web page of application, allowing a wider exploration and accelerating the process of applying the sanitizers on the untrusted variables of web application. The XSS attack mitigation capability of our framework was evaluated on web applications deployed for the cloud users in the cloud environment. The experimental results reveal that this technique detects the XSS attack payloads with minimum rate of false negatives and less runtime overhead
CIMTDetect: A Community Infused Matrix-Tensor Coupled Factorization Based Method for Fake News Detection
Detecting whether a news article is fake or genuine is a crucial task in
today's digital world where it's easy to create and spread a misleading news
article. This is especially true of news stories shared on social media since
they don't undergo any stringent journalistic checking associated with main
stream media. Given the inherent human tendency to share information with their
social connections at a mouse-click, fake news articles masquerading as real
ones, tend to spread widely and virally. The presence of echo chambers (people
sharing same beliefs) in social networks, only adds to this problem of
wide-spread existence of fake news on social media. In this paper, we tackle
the problem of fake news detection from social media by exploiting the very
presence of echo chambers that exist within the social network of users to
obtain an efficient and informative latent representation of the news article.
By modeling the echo-chambers as closely-connected communities within the
social network, we represent a news article as a 3-mode tensor of the structure
- and propose a tensor factorization based method to
encode the news article in a latent embedding space preserving the community
structure. We also propose an extension of the above method, which jointly
models the community and content information of the news article through a
coupled matrix-tensor factorization framework. We empirically demonstrate the
efficacy of our method for the task of Fake News Detection over two real-world
datasets. Further, we validate the generalization of the resulting embeddings
over two other auxiliary tasks, namely: \textbf{1)} News Cohort Analysis and
\textbf{2)} Collaborative News Recommendation. Our proposed method outperforms
appropriate baselines for both the tasks, establishing its generalization.Comment: Presented at ASONAM'1
Quantum entropy expansion using n-qubit permutation matrices in Galois field
Random numbers are critical for any cryptographic application. However, the
data that is flowing through the internet is not secure because of entropy
deprived pseudo random number generators and unencrypted IoTs. In this work, we
address the issue of lesser entropy of several data formats. Specifically, we
use the large information space associated with the n-qubit permutation
matrices to expand the entropy of any data without increasing the size of the
data. We take English text with the entropy in the range 4 - 5 bits per byte.
We manipulate the data using a set of n-qubit (n 10) permutation
matrices and observe the expansion of the entropy in the manipulated data (to
more than 7.9 bits per byte). We also observe similar behaviour with other data
formats like image, audio etc. (n 15)
Cryptanalysis of quantum permutation pad
Cryptanalysis increases the level of confidence in cryptographic algorithms.
We analyze the security of a symmetric cryptographic algorithm - quantum
permutation pad (QPP) [8]. We found the instances of ciphertext the same as
plaintext even after the action of QPP with the probability 1/N when the entire
set of permutation matrices of dimension N is used and with the probability
1/N^m when an incomplete set of m permutation matrices of dimension N are used.
We visually show such instances in a cipher image created by QPP of 256
permutation matrices of different dimensions. For any practical usage of QPP,
we recommend a set of 256 permutation matrices of a dimension more or equal to
2048.Comment: 7 pages, 1 figures, comments are welcom
Comparative study of antibacterial activity of two different earthworm species, Perionyx excavatus and Pheretima posthuma against pathogenic bacteria
Disease outbreaks are being increasingly recognized as a significant constraint on aquaculture production and trade affecting the economic development of the sector in many countries. Extracting and using biologically active compounds from earthworms has traditionally been practiced by indigenous people throughout the world. The aim of the present study was to shown antimicrobial activity through earthworm extract against fish bacterial pathogens. In total, 8 bacterial strains i.e. 6 gram negative viz. Aeromonas hydrophila, Pseudomonas aeruginosa, P. fluorescens, E.coli, Enterobacter aerogens and Shigella sp. and 2 gram positive viz. Staphylococcus aureus and Micrococcus luteus were identified. The extract of earthworm Perionyx excavatus, Pheretima posthuma were prepared and antimicrobial activity of the extract was determined by antimicrobial well diffusion assay. After 24 hrs of incubation period, it was observed that earthworm extract showed antibacterial activity against isolated bacterial strains. Among earthworm extract of two different species, the maximum zone of inhibition was shown against A. hydrophila by Perionyx excavatus (18.33± 0.66 mm) and P. posthuma (16.66±0.33). P. excavatus showed antibacterial activity against all pathogenic bacteria except Shigella spp. However on the other hand, P.posthuma showed antibacterial activity against A. hydrophila, P. fluorescens, E.coli, and S. aureus. The study has proved that earthworm extract can be effectively used for suppression of bacterial infection in fishes and that it can used as potential antimicrobial drug against commercial antibiotic resistance bacteria
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