Secured & High Resolution Watermarking Technique

the watermarking is a method of embedding some king of hidden authentication information with cover image so that it can be identified later. There are many methods available which uses some kind of signal or the binary images, however sometimes it is difficult to defend that the recovered signal/image is same embedded watermarked image because there is always a possibility to get similar patterns form non watermarked images, hence in this paper we presents a secure watermark technique which is capable to embed 8 bit image. The experimental results shows that the technique is not only time efficient but also immune to different attacks

To study the correlation between red cell distribution width and left ventricular ejection fraction in patients of acute myocardial infraction

Background: Ischemic heart disease (IHD) is a condition in which there is an inadequate supply of blood and oxygen to a portion of myocardium. The objective of study was to assess the correlation between red cell distribution width and left ventricular ejection fraction in patients presenting with acute myocardial infarction.Methods: Study was conducted on 200 patients admitted at tertiary care centre with acute myocardial infarction satisfying inclusion criteria. Detailed history and clinical examination was done. RDW and other CBC parameters were calculated by an automatic blood counter and measurement of LVEF done by 2D-echocardiography.Results: Out of 200 patients of acute myocardial infarction 178 (89%) were male and 22 (11%) were female. Both RDW and LVEF are linked in patients of acute myocardial infarctions, as there was statistically significant correlation between high RDW and low LVEF (P <0.01, r - value 0.432).Conclusions: It is observed that increase RDW and decrease LVEF were linked together which is statistically significant. RDW can be used to assess severity and outcome in patients of acute myocardial infraction on their initial presentation especially at peripheral health centre where echocardiography is not available routinely

Design and Implementation of an Innovative Internet of Things (IOT) based Smart Energy Meter

Energy meter is very essential measuring instrument for measuring the power in domestic, industrial etc. environment. Correct and appropriate measuring of power without any error is important in order to calculate the total power consumption and then for tariff calculation. In view of this, in this paper design and implementation on an innovative smart energy meter is proposed. The proposed smart energy meter is based on Internet of Things (IoT) applications. The paper describes its design along with its working

A Comprehensive Review of Smart Energy Meters: An Innovative Approach

Energy meter is an important device used for measuring the power. It is used in customers� homes, industries etc. for measuring the electrical power. A lot of modifications and development has taken place in the construction and operation of the energy meters over a decade. In view of above this paper presents a review of the development of the energy meters and their applications. Energy meters and its different types along with the innovation in this field is been discussed in this paper

Internet of Things (IoT): Research, Architectures and Applications

Internet of Things is the concept of connecting any device (so long as it has an on/off switch) to the Internet and to other connected devices. The IoT is a giant network of connected things and people, all of which collect and share data about the way they are used and about the environment around them. Experts estimate that the IoT will consist of about 30 billion objects by 2020. This paper presents a study based on IoT and its applications in different field of science and technology. Along with the introduction of the IoT literature review is also provided. The paper also discusses the architecture and elements of the IoT along with its different applications

Max-Quantile Grouped Infinite-Arm Bandits

In this paper, we consider a bandit problem in which there are a number of groups each consisting of infinitely many arms. Whenever a new arm is requested from a given group, its mean reward is drawn from an unknown reservoir distribution (different for each group), and the uncertainty in the arm's mean reward can only be reduced via subsequent pulls of the arm. The goal is to identify the infinite-arm group whose reservoir distribution has the highest $(1-\alpha)$-quantile (e.g., median if $\alpha = \frac{1}{2}$), using as few total arm pulls as possible. We introduce a two-step algorithm that first requests a fixed number of arms from each group and then runs a finite-arm grouped max-quantile bandit algorithm. We characterize both the instance-dependent and worst-case regret, and provide a matching lower bound for the latter, while discussing various strengths, weaknesses, algorithmic improvements, and potential lower bounds associated with our instance-dependent upper bounds

CASPR: Customer Activity Sequence-based Prediction and Representation

Tasks critical to enterprise profitability, such as customer churn prediction, fraudulent account detection or customer lifetime value estimation, are often tackled by models trained on features engineered from customer data in tabular format. Application-specific feature engineering adds development, operationalization and maintenance costs over time. Recent advances in representation learning present an opportunity to simplify and generalize feature engineering across applications. When applying these advancements to tabular data researchers deal with data heterogeneity, variations in customer engagement history or the sheer volume of enterprise datasets. In this paper, we propose a novel approach to encode tabular data containing customer transactions, purchase history and other interactions into a generic representation of a customer's association with the business. We then evaluate these embeddings as features to train multiple models spanning a variety of applications. CASPR, Customer Activity Sequence-based Prediction and Representation, applies Transformer architecture to encode activity sequences to improve model performance and avoid bespoke feature engineering across applications. Our experiments at scale validate CASPR for both small and large enterprise applications.Comment: Presented at the Table Representation Learning Workshop, NeurIPS 2022, New Orleans. Authors listed in random orde

Federated Graph Representation Learning using Self-Supervision

Federated graph representation learning (FedGRL) brings the benefits of distributed training to graph structured data while simultaneously addressing some privacy and compliance concerns related to data curation. However, several interesting real-world graph data characteristics viz. label deficiency and downstream task heterogeneity are not taken into consideration in current FedGRL setups. In this paper, we consider a realistic and novel problem setting, wherein cross-silo clients have access to vast amounts of unlabeled data with limited or no labeled data and additionally have diverse downstream class label domains. We then propose a novel FedGRL formulation based on model interpolation where we aim to learn a shared global model that is optimized collaboratively using a self-supervised objective and gets downstream task supervision through local client models. We provide a specific instantiation of our general formulation using BGRL a SoTA self-supervised graph representation learning method and we empirically verify its effectiveness through realistic cross-slio datasets: (1) we adapt the Twitch Gamer Network which naturally simulates a cross-geo scenario and show that our formulation can provide consistent and avg. 6.1% gains over traditional supervised federated learning objectives and on avg. 1.7% gains compared to individual client specific self-supervised training and (2) we construct and introduce a new cross-silo dataset called Amazon Co-purchase Networks that have both the characteristics of the motivated problem setting. And, we witness on avg. 11.5% gains over traditional supervised federated learning and on avg. 1.9% gains over individually trained self-supervised models. Both experimental results point to the effectiveness of our proposed formulation. Finally, both our novel problem setting and dataset contributions provide new avenues for the research in FedGRL.Comment: FedGraph'22 workshop (non archival) version. (https://sites.google.com/view/fedgraph2022/accepted-papers

Complications of Mandibular Distraction Osteogenesis in Infants with Isolated Robin Sequence

Mandibular Distraction Osteogenesis (MDO) is now the preferred procedure to alleviate airway obstruction in infants with severe Robin Sequence (RS). However, there have been very few studies investigating complications related to MDO surgery performed on patients affected by isolated RS. In this study, age at distraction, weight at distraction, preoperative intubation, repeat MDO and complications associated with MDO were included as variables. Minor, moderate and major problems were evaluated and recorded as surgical site infections (SSI), injuries to the facial nerve, self-extinction hypertrophic scars, temporomandibular joint ankylosis, device failures, early ossification and fibrous non-union. One hundred and fifty one patients with isolated RS were included. At distraction, the mean age was 72 days (12–540 days) and the mean weight was 4.05 kg (2.4–12.2 kg). Only one patient needed tracheostomy after MDO, and none required further distraction. Ultimately, the complication rate was 15.23%, and there was a total of 7.95% minor, 9.27% moderate and 0% major complications. Minor incidents included surgical site infection (SSI) managed with antibiotics taken orally (n = 8), neuropraxia in the VII cranial nerve (CN) (n = 1), and hypertrophic scarring (n = 3). Incidents reported as moderate were SSIs managed with intravenous antibiotics (n = 9), incision and drainage (n = 3) and self-extubation (n = 2). There was no case of TMJ ankylosis. There were no cases of early or premature ossification, fibrous non-union and device fracture. In conclusion, MDO is an effective and appropriate management technique for infants with isolated RS and severe airway obstruction. Infections at the surgery site accounted for the vast majority of the complications. Further investigations may be needed to determine the long-term consequences of MDO