Cohort-based kernel principal component analysis with Multi-path Service Routing in Federated Learning

Federated Learning (FL) is a machine learning (ML) strategy that is performed in a decentralized environment. The training is performed locally by the client on the global model shared by the server. Federated learning has recently been used as a service (FLaaS) to provide a collaborative training environment to independent third-party applications. However, the widespread adoption in distributed settings of FL has opened venues for a number of security attacks. A number of studies have been performed to prevent multiple FL attacks. However, sophisticated attacks, such as label-flipping attacks, have received little or no attention. From the said perspective, this research is focused on providing a defense mechanism for the aforesaid attack. The proposed approach is based on Type-based Cohorts (TC) with Kernel Principal Component Analysis (KPCA) to detect and defend against label-flipping attacks. Moreover, to improve the performance of the network, we will deploy Multi-path Service Routing (MSR) for edge nodes to work effectively. The KPCA will be used to secure the network from attacks. The proposed mechanism will provide an effective and secure FL system. The proposed approach is evaluated with respect to the following measures: execution time, memory consumption, information loss, accuracy, service request violations, and the request’s waiting time

Ecofriendly Dyes: Extraction, Characterization and Potential Applications

Indians were thought to be forerunners in the technique of natural dyeing. Although indigenous knowledge systems have been practiced for many years, the usage of natural dyes has declined over generations owing to a lack of documentation and accurate understanding of the extraction and dyeing processes. As a result, natural dyes aren't commercially viable. Currently, all ecologically hazardous synthetic chemical dyes are utilized to colour textile fabrics. They are nonbiodegradable, carcinogenic, and cause water contamination and waste disposal issues. Natural colours provide a viable answer to these issues. Natural dyes are used to colour textiles, meals, medicines, and cosmetics. Dyes are also used in small amounts to colour paper, leather, shoe polish, wood, cane, candles, and other materials. Historically, dyes were obtained only from natural sources. Natural dyes, on the other hand, suffer from the inherent limitations of uniform application and dye standardization, since dyes obtained from comparable plants or natural sources are impacted and exposed to the vagaries of climate, soil, cultivation practices, and so on. As a result, standardization procedures play a critical and essential role for natural dyes to be properly commercialized and compete with synthetic dyes. This study is all about natural dyes, their extraction, characterization, applications, and their uses

A prospective and observational study on complications of type 2 diabetes mellitus in correlation with body mass index

Background: The aim of this study is to observe the prevalence of complications of diabetes mellitus (Type 2) among patients and to minimize their occurrence through patient education. The study helps to assess the clinical data of patients with diabetes mellitus (Type 2) along with the analysis of patterns, frequencies and predictive factors of microvascular, macrovascular complications and to educate and minimize the complications of diabetes mellitus among patients.Methods: Prospective and observational study was conducted among the type 2 diabetes mellitus patients at Sree Diabetes Clinic for a period of 6 months. The patients were interviewed using the patient data collection form which included demographic details, chief complaints and different diagnostic tools to detect type of complications. Both micro and macrovascular complications were evaluated.Results: A total of 150 type 2 diabetic cases were collected. Out of these 38(25.33%) patients were having BMI <25, and 112(74.67%) were having BMI ≥25 (overweight and obese). Out of 150 diabetic cases collected, a total of 131 diabetic complications were found. Out of these, 64(42.6%) were neuropathy, 3(2%) were nephropathy, 20(13.3%) were retinopathy and 17(11.3%) were having cardiovascular complications. Out of 112 patients with BMI ≥25, 60(54%) were found to have diabetic complications and out of 38 patients with BMI <25, 18(47%) were found to have diabetic complications. 90 out of 150 patients had a history of hypertension and 17 out of 150 patients had an abnormal lipid level.Conclusions: In this study, author found that obesity is a major risk factor for the development of diabetes mellitus and its complications

A framework for intracranial saccular aneurysm detection and quantification using morphological analysis of cerebral angiograms

Reliable early prediction of aneurysm rupture can greatly help neurosurgeons to treat aneurysms at the right time, thus saving lives as well as providing significant cost reduction. Most of the research efforts in this respect involve statistical analysis of collected data or simulation of hemodynamic factors to predict the risk of aneurysmal rupture. Whereas, morphological analysis of cerebral angiogram images for locating and estimating unruptured aneurysms is rarely considered. Since digital subtraction angiography (DSA) is regarded as a standard test by the American Stroke Association and American College of Radiology for identification of aneurysm, this paper aims to perform morphological analysis of DSA to accurately detect saccular aneurysms, precisely determine their sizes, and estimate the probability of their ruptures. The proposed diagnostic framework, intracranial saccular aneurysm detection and quantification, first extracts cerebrovascular structures by denoising angiogram images and delineates regions of interest (ROIs) by using watershed segmentation and distance transformation. Then, it identifies saccular aneurysms among segmented ROIs using multilayer perceptron neural network trained upon robust Haralick texture features, and finally quantifies aneurysm rupture by geometrical analysis of identified aneurysmic ROI. De-identified data set of 59 angiograms is used to evaluate the performance of algorithms for aneurysm detection and risk of rupture quantification. The proposed framework achieves high accuracy of 98% and 86% for aneurysm classification and quantification, respectively

Re-defining the Empirical ZZ Ceti Instability Strip

We use the new ZZ Ceti stars (hydrogen atmosphere white dwarf variables; DAVs) discovered within the Sloan Digital Sky Survey (Mukadam et al. 2004) to re-define the empirical ZZ Ceti instability strip. This is the first time since the discovery of white dwarf variables in 1968 that we have a homogeneous set of spectra acquired using the same instrument on the same telescope, and with consistent data reductions, for a statistically significant sample of ZZ Ceti stars. The homogeneity of the spectra reduces the scatter in the spectroscopic temperatures and we find a narrow instability strip of width ~950K, from 10850--11800K. We question the purity of the DAV instability strip as we find several non-variables within. We present our best fit for the red edge and our constraint for the blue edge of the instability strip, determined using a statistical approach.Comment: 14 pages, 5 pages, ApJ paper, accepte

A Robust Internet of Drones Security Surveillance Communication Network Based on IOTA

cations. The rise in drone usage underscores privacy and security challenges concerning flight boundaries, data collection in public and private domains, and data storage and dissemination. Such issues highlight the drones’ capability to communicate and securely store data over potentially insecure channels. Recognizing these challenges and gaps in the research, this paper introduces an efficient and secure security surveillance model for the Internet of Drones (IoD). Our model ensures secure communication between Ground Stations (GS) and Drones, effectively addressing various attack types. Particularly, surveillance drones are vulnerable to physical capture attacks. We delve into a scenario where a network drone is physically apprehended. Leveraging the information stored within the drone, the attacker could potentially access the session. This paper proposes a solution to counter such threats. Through experiments using MATLAB and VScode, we evaluate our network’s efficiency and scalability in relation to the surge in transactions. The findings reveal our model’s prowess in handling large-scale networks. Specifically, when transactions surpass 1000 per minute, our model achieves approximately a 20% reduction in processing time compared to existing studies. Moreover, our approach facilitates about 80% enhanced communication efficiency relative to the contemporary state- of-the-art frameworks. A security analysis via AVISPA further corroborates the robustness and security of our proposed communication strategy against diverse attack types

Effects of Variation of Quantum Well Numbers on Gain Characteristics of Type-I InGaAsP/InP Nano-heterostructure

This paper reports the effects of variation of number of quantum wells in material gain characteristics and lasing wavelength of step index separately confined type-I InGaAsP/InP lasing nano-heterostructure for different carrier concentrations at room temperature in TE (Transverse Electric) mode of polarization. Peak material gain is found to be highest when the number of quantum well is one in the structure. However, for the case of 3QWs, 5QWs and 7QWs, it is almost same at a particular carrier density. Lasing wavelength at peak material gain considerably increases as the number of quantum well layers vary from single quantum well layer to three quantum well layers in the active region and after that it will remain almost same by any further increase in number of quantum wells for a particular carrier density. Furthermore, negative gain condition in the material gain spectra exists in the case of multiple quantum wells only at carrier concentration of 2×1018/cm3. The results suggest that the proposed nano-heterostructure is highly suitable as a light source in fiber optic links for long distance communication

Discovery of a Nova-Like Cataclysmic Variable in the Kepler Mission Field

We announce the identification of a new cataclysmic variable star in the field of the Kepler Mission, KIC J192410.81+445934.9. This system was identified during a search for compact pulsators in the Kepler field. High-speed photometry reveals coherent large-amplitude variability with a period of 2.94 h. Rapid, large-amplitude quasi-periodic variations are also detected on time scales of ~1200 s and ~650 s. Time-resolved spectroscopy covering one half photometric period shows shallow, broad Balmer and He I absorption lines with bright emission cores as well as strong He II and Bowen blend emission. Radial velocity variations are also observed in the Balmer and He I emission lines that are consistent with the photometric period. We therefore conclude that KIC J192410.81+445934.9 is a nova-like variable of the UX UMa class in or near the period gap, and it may belong to the rapidly growing subclass of SW Sex systems. Based on 2MASS photometry and companion star models, we place a lower limit on the distance to the system of ~500 pc. Due to limitations of our discovery data, additional observations including spectroscopy and polarimetry are needed to confirm the nature of this object. Such data will help to further understanding of the behavior of nova-like variables in the critical period range of 3-4 h, where standard cataclysmic variable evolutionary theory finds major problems. The presence of this system in the Kepler mission field-of-view also presents a unique opportunity to obtain a continuous photometric data stream of unparalleled length and precision on a cataclysmic variable system.Comment: Accepted for publication in the Astronomical Journal. 8 pages, 7 figures, uses emulateapj

Tactile Discrimination Using Template Classifiers: Towards a Model of Feature Extraction in Mammalian Vibrissal Systems

Rats and other whiskered mammals are capable of making sophisticated sensory discriminations using tactile signals from their facial whiskers (vibrissae). As part of a programme of work to develop biomimetic technologies for vibrissal sensing, including whiskered robots, we are devising algorithms for the fast extraction of object parameters from whisker deflection data. Previous work has demonstrated that radial distance to contact can be estimated from forces measured at the base of the whisker shaft. We show that in the case of a moving object contacting a whisker, the measured force can be ambiguous in distinguishing a nearby object moving slowly from a more distant object moving rapidly. This ambiguity can be resolved by simultaneously extracting object position and speed from the whisker deflection time series – that is by attending to the dynamics of the whisker’s interaction with the object. We compare a simple classifier with an adaptive EM (Expectation Maximisation) classifier. Both systems are effective at simultaneously extracting the two parameters, the EM-classifier showing similar performance to a handpicked template classifier. We propose that adaptive classification algorithms can provide insights into the types of computations performed in the rat vibrissal system when the animal is faced with a discrimination task

Distributed Computing Grid Experiences in CMS

The CMS experiment is currently developing a computing system capable of serving, processing and archiving the large number of events that will be generated when the CMS detector starts taking data. During 2004 CMS undertook a large scale data challenge to demonstrate the ability of the CMS computing system to cope with a sustained data-taking rate equivalent to 25% of startup rate. Its goals were: to run CMS event reconstruction at CERN for a sustained period at 25 Hz input rate; to distribute the data to several regional centers; and enable data access at those centers for analysis. Grid middleware was utilized to help complete all aspects of the challenge. To continue to provide scalable access from anywhere in the world to the data, CMS is developing a layer of software that uses Grid tools to gain access to data and resources, and that aims to provide physicists with a user friendly interface for submitting their analysis jobs. This paper describes the data challenge experience with Grid infrastructure and the current development of the CMS analysis system