Prominent visual treatment of high quality text in an advertisement

Online advertisements typically get only a limited amount of time when a user views the ad alongside other content. Consequently, visual format of the ad is important in attracting the user’s attention to ad content that will help the user make an informed decision. Visual cues in an ad, such as color, font size, etc. help advertisers highlight the most relevant information such as special discounts and offers. Techniques described here automatically extract the most relevant content from an advertiser-provided ad creative. The ad is formatted such that the extracted content is displayed in a prominent manner, thus improving the likelihood that the user clicks through

Promoting High Quality Ad Data From Less-Prominent Formats To More-Prominent Representation

A system and method are disclosed that extracts high quality data from less-prominent text format to a more-prominent text format. The system extracts high quality advertiser-provided data found in low-prominent text formats such as callouts and content within site links. The extracted information is combined with available ad content present in user-noticeable text formats that are highly prominent. The information may include price info (in price extension), shipping info, service availability, return/pickup information, benefits, etc. When the user makes a commercial query, the system presents the combined ad content in the prominent text formats. The disclosed system increases the visibility of high quality information provided in the advertisement, which gets across to the user within a short time

Leveraging High Quality Information To Generate New Ad Content

A system and method are disclosed that leverages high quality information present in ads of a particular advertiser to provide content for ads that may appear during a search. The system includes a server which stores ads under a particular category to determine and extract relevant high quality information from a group of ads provided by an advertiser. When a user presents a commercial query, the system collects high quality information from stored ads for formatting into a new ad. The ad is then displayed to the user. This system thus automatically generates high quality data for ad content

Enhancing Ads By Appending High Quality Text From Other Ads

A system and method are disclosed that enhance an ad by appending high quality text from other ads with the same theme. When the user makes a commercial query , the system determines user requirement and analyzes relevant ad content present in existing ads within a database to display ads to the user. The system then optimizes the ad content before displaying the ad to the user. An auction may be carried out to decide on the information to be displayed with the chosen ad. The system ensures that ads with relevant high quality information are presented to the user by utilizing the available space allotted. The user benefits as he sees more relevant information in the ad before clicking on the ad and the advertiser need not manually add information to each ad

Personalized Advertisement Content

This document describes a system where advertisers do not need to create multiple variations of an advertisement for targeting different sets of users. The system includes utilizing machine learning algorithms to generate a dedicated model to generalize user interests (e.g., as opposed to a rule based approach). The machine learning algorithms and model can be applied to a large scale of users to provide coverage on personalized advertisements. As the advertisement is generated at query time, additional storage is not needed in the backend

Ontology-based Classification and Analysis of non- emergency Smart-city Events

Several challenges are faced by citizens of urban centers while dealing with day-to-day events, and the absence of a centralised reporting mechanism makes event-reporting and redressal a daunting task. With the push on information technology to adapt to the needs of smart-cities and integrate urban civic services, the use of Open311 architecture presents an interesting solution. In this paper, we present a novel approach that uses an existing Open311 ontology to classify and report non-emergency city-events, as well as to guide the citizen to the points of redressal. The use of linked open data and the semantic model serves to provide contextual meaning and make vast amounts of content hyper-connected and easily-searchable. Such a one-size-fits-all model also ensures reusability and effective visualisation and analysis of data across several cities. By integrating urban services across various civic bodies, the proposed approach provides a single endpoint to the citizen, which is imperative for smooth functioning of smart cities

LFSR Next Bit Prediction through Deep Learning

Pseudorandom bit sequences are generated using deterministic algorithms to simulate truly random sequences. Many cryptographic algorithms use pseudorandom sequences, and the randomness of these sequences greatly impacts the robustness of these algo-rithms. Important crypto primitive Linear Feedback Shift Register (LFSR) and its combina-tions have long been used in stream ciphers for the generation of pseudorandom bit sequences. The sequences generated by LFSR can be predicted using the traditional Ber-lekamp Massey Algorithm, which solves LFSR in 2×n number of bits, where n is the de-gree of LFSR. Many different techniques based on ML classifiers have been successful at predicting the next bit of the sequences generated by LFSR. However, the main limitation in the existing approaches is that they require a large number (as compared to the de-gree of LFSR) of bits to solve the LFSR. In this paper, we have proposed a novel Pattern Duplication technique that exponentially reduces the input bits requirement for training the ML Model. This Pattern Duplication technique generates new samples from the available data using two properties of the XOR function used in LFSRs. We have used the Deep Neural Networks (DNN) as the next bit predictor of the sequences generated by LFSR along with the Pattern Duplication technique. Due to the Pattern Duplication tech-nique, we need a very small number of input patterns for DNN. Moreover, in some cases, the DNN model managed to predict LFSRs in less than 2n bits as compared to the Ber-lekamp Massey Algorithm. However, this technique was not successful in cases where LFSRs have primitive polynomials with a higher number of tap points

Chronic pain evaluation in breast cancer patients using the Self-Report Leeds Assessment of Neuropathic Symptoms and Signs (S-LANSS): a single center cross-sectional retrospective study

Background: Breast cancer is the most common cancer in India, and the number of survivors has increased over the last few years. Pain is one of the most common symptoms during cancer treatment due to either the disease itself or adverse effects of treatment. The available data suggests that breast cancer patients have a high prevalence of neuropathic pain. Patients and methods: A cross sectional observational study was done at the Department of Radiation Oncology, between November 2021 to June 2022. The patients were admitted and screened for participation, non-metastatic post operative breast cancer on regular follow up for 2 years after their last chemotherapy or radiotherapy and not having any chronic neuropathy disease and the Self-Report Leeds Assessment of Neuropathic Symptoms and Signs (S-LANSS) pain scale was used to assess the neuropathy pain status of patients. Patients’ demographics, clinical characteristics, and treatment of surgery, radiation therapy, and chemotherapy were collected and the comparison of the pain score between the patients was analysed. Results: Total of 149 patients were included in the study. S-LANSS score was calculated in the study population and more than 61% of participants reported a score equal or greater than 12, suggesting a predominant neuropathic pain component. Autonomic dysfunction, thermal pain, and allodynia were more prevalent in patients who underwent mastectomies compared to breast-conserving surgery. Whereas the dysesthesia and autonomic dysfunction score was higher in only the anthracycline group. Conclusions: The most important index for quality of life in cancer patients is the presence of persistent chronic pain and it is important to classify it accordingly in order to provide the best management. Using the S-LANSS score, the pattern of neuropathic pain can be determined early which leads to early intervention

Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study

Background Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave. Methods This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs. Results Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI − 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI − 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates. Conclusions Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021)