328 research outputs found
Extended 16x16 Play-Fair Algorithm for Secure Key Exchange Using RSA Algorithm
With the world entering in the 21st century rigorous efforts are being made to secure data and flow of information among the users. Though with the advancements are fast and efficient the third party intervention and security threats has also increased many folds. The algorithms being used to encrypt and decrypt data needs to be strong enough to secure the data but also simple enough for a user to handle the process. With this article a novel, practical approach is presented which not only makes the information more secured but also being based on RSA algorithm is easy enough for users to understand and implement into the systems
Memex: a browsing assistant for collaborative archiving and mining of surf trails
Keyword indices, topic directories and link-based rankings are used to search and structure the rapidly growing Web today. Surprisingly little use is made of years of browsing experience of millions of people. Indeed, this information is routinely discarded by browsers. Even deliberate bookmarks are stored in a passive and isolated manner. All this goes against Vannevar Bush’s dream of the Memex: An enhanced supplement to personal and community memory. We propose to demonstrate the beginnings of a ‘Memex’ for the Web: A browsing assistant for individuals and groups with focused interests. Memex blurs the artificial distinction between browsing history and deliberate bookmarks. The resulting glut of data is analyzed in a number of ways at the individual and community levels. Memex constructs a topic directory customized to the community, mapping their interests naturally to nodes in this directory. This lets the user recall topic-based browsing contexts by asking questions like “What trails was I following when I was last surfing about classical music?” and “What are some popular pages in or near my community’s recent trail graph related to music?
Machine Learning for Microcontroller-Class Hardware -- A Review
The advancements in machine learning opened a new opportunity to bring
intelligence to the low-end Internet-of-Things nodes such as microcontrollers.
Conventional machine learning deployment has high memory and compute footprint
hindering their direct deployment on ultra resource-constrained
microcontrollers. This paper highlights the unique requirements of enabling
onboard machine learning for microcontroller class devices. Researchers use a
specialized model development workflow for resource-limited applications to
ensure the compute and latency budget is within the device limits while still
maintaining the desired performance. We characterize a closed-loop widely
applicable workflow of machine learning model development for microcontroller
class devices and show that several classes of applications adopt a specific
instance of it. We present both qualitative and numerical insights into
different stages of model development by showcasing several use cases. Finally,
we identify the open research challenges and unsolved questions demanding
careful considerations moving forward.Comment: Accepted for publication at IEEE Sensors Journa
Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ Cameras
Existing approaches for autonomous control of pan-tilt-zoom (PTZ) cameras use
multiple stages where object detection and localization are performed
separately from the control of the PTZ mechanisms. These approaches require
manual labels and suffer from performance bottlenecks due to error propagation
across the multi-stage flow of information. The large size of object detection
neural networks also makes prior solutions infeasible for real-time deployment
in resource-constrained devices. We present an end-to-end deep reinforcement
learning (RL) solution called Eagle to train a neural network policy that
directly takes images as input to control the PTZ camera. Training
reinforcement learning is cumbersome in the real world due to labeling effort,
runtime environment stochasticity, and fragile experimental setups. We
introduce a photo-realistic simulation framework for training and evaluation of
PTZ camera control policies. Eagle achieves superior camera control performance
by maintaining the object of interest close to the center of captured images at
high resolution and has up to 17% more tracking duration than the
state-of-the-art. Eagle policies are lightweight (90x fewer parameters than
Yolo5s) and can run on embedded camera platforms such as Raspberry PI (33 FPS)
and Jetson Nano (38 FPS), facilitating real-time PTZ tracking for
resource-constrained environments. With domain randomization, Eagle policies
trained in our simulator can be transferred directly to real-world scenarios.Comment: 20 pages, IoTD
Exploring body composition metrics: Comparing percentage body fat, BMI, and body fat mass in college students
Purpose: The study's objective was to compare the chosen Physiological variables indicating Obesity Status among Physical Education Students and Humanities Students.
Methodology: For the purpose of the study, 21 participants (8 Physical Education Students and 13 Humanities Students) of age 20-24 years were chosen from Department of Physical Education and Sports and Department of Sociology of Central University of Haryana, Mahendargarh. To achieve the study's goals, simple random sampling technique was used, Body Composition Analyzer a leading Physiological assessment tool was used for measuring parameters named Body Mass Index (BMI), Body Fat Mass (BFM) & Percent Body Fat (PBF), of students of Physical Education and Humanities. As a statistical method, the independent sample "T" test was used.
Findings: A 0.05 alpha level was chosen. Because the t value was insignificant (p>0.05), the statistical analysis of the results and comparison of the two groups revealed no statistically significant
difference in mean Body Fat Mass (BFM), Percent Body Fat (PBF), and Body Mass Index
(BMI). The outcome demonstrated that the similarity between these parameters was either due to similar Diet provided by the University to both the groups in the University Hostel Mess or the daily long-distance walking done by the Physical Education as well as the Humanities students from the Hostels to their respective classes
Synergistic association of STX1A and VAMP2 with cryptogenic epilepsy in North Indian population
Introduction “Common epilepsies”, merely explored for genetics are the most
frequent, nonfamilial, sporadic cases in hospitals. Because of their much
debated molecular pathology, there is a need to focus on other neuronal
pathways including the existing ion channels. Methods For this study, a total
of 214 epilepsy cases of North Indian ethnicity comprising 59.81% generalized,
40.19% focal seizures, and based on epilepsy types, 17.29% idiopathic, 37.38%
cryptogenic, and 45.33% symptomatic were enrolled. Additionally, 170 unrelated
healthy individuals were also enrolled. Here, we hypothesize the involvement
of epilepsy pathophysiology genes, that is, synaptic vesicle cycle, SVC genes
(presynapse), ion channels and their functionally related genes (postsynapse).
An interactive analysis was initially performed in SVC genes using multifactor
dimensionality reduction (MDR). Further, in order to understand the influence
of ion channels and their functionally related genes, their interaction
analysis with SVC genes was also performed. Results A significant interactive
two-locus model of STX1A_rs4363087|VAMP2_rs2278637 (presynaptic genes) was
observed among SVC variants in all epilepsy cases (P1000-value = 0.054; CVC =
9/10; OR = 2.86, 95%CI = 1.88–4.35). Further, subgroup analysis revealed
stronger interaction for the same model in cryptogenic epilepsy patients only
(P1000-value = 0.012; CVC = 10/10; OR = 4.59, 95%CI = 2.57–8.22). However,
interactive analysis of presynaptic and postsynaptic genes did not show any
significant association. Conclusions Significant synergistic interaction of
SVC genes revealed the possible functional relatedness of presynapse with
pathophysiology of cryptogenic epilepsy. Further, to establish the clinical
utility of the results, replication in a large and similar phenotypic group of
patients is warranted
Analysis of conformational variation in macromolecular structural models
Experimental conditions or the presence of interacting components can lead to variations in the structural models of macromolecules. However, the role of these factors in conformational selection is often omitted by in silico methods to extract dynamic information from protein structural models. Structures of small peptides, considered building blocks for larger macromolecular structural models, can substantially differ in the context of a larger protein. This limitation is more evident in the case of modeling large multi-subunit macromolecular complexes using structures of the individual protein components. Here we report an analysis of variations in structural models of proteins with high sequence similarity. These models were analyzed for sequence features of the protein, the role of scaffolding segments including interacting proteins or affinity tags and the chemical components in the experimental conditions. Conformational features in these structural models could be rationalized by conformational selection events, perhaps induced by experimental conditions. This analysis was performed on a non-redundant dataset of protein structures from different SCOP classes. The sequence-conformation correlations that we note here suggest additional features that could be incorporated by in silico methods to extract dynamic information from protein structural models
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