Survey on security issues in file management in cloud computing environment

Cloud computing has pervaded through every aspect of Information technology in past decade. It has become easier to process plethora of data, generated by various devices in real time, with the advent of cloud networks. The privacy of users data is maintained by data centers around the world and hence it has become feasible to operate on that data from lightweight portable devices. But with ease of processing comes the security aspect of the data. One such security aspect is secure file transfer either internally within cloud or externally from one cloud network to another. File management is central to cloud computing and it is paramount to address the security concerns which arise out of it. This survey paper aims to elucidate the various protocols which can be used for secure file transfer and analyze the ramifications of using each protocol.Comment: 5 pages, 1 tabl

Secure management of logs in internet of things

Ever since the advent of computing, managing data has been of extreme importance. With innumerable devices getting added to network infrastructure, there has been a proportionate increase in the data which needs to be stored. With the advent of Internet of Things (IOT) it is anticipated that billions of devices will be a part of the internet in another decade. Since those devices will be communicating with each other on a regular basis with little or no human intervention, plethora of real time data will be generated in quick time which will result in large number of log files. Apart from complexity pertaining to storage, it will be mandatory to maintain confidentiality and integrity of these logs in IOT enabled devices. This paper will provide a brief overview about how logs can be efficiently and securely stored in IOT devices.Comment: 6 pages, 1 tabl

GPT-InvestAR: Enhancing Stock Investment Strategies through Annual Report Analysis with Large Language Models

Annual Reports of publicly listed companies contain vital information about their financial health which can help assess the potential impact on Stock price of the firm. These reports are comprehensive in nature, going up to, and sometimes exceeding, 100 pages. Analysing these reports is cumbersome even for a single firm, let alone the whole universe of firms that exist. Over the years, financial experts have become proficient in extracting valuable information from these documents relatively quickly. However, this requires years of practice and experience. This paper aims to simplify the process of assessing Annual Reports of all the firms by leveraging the capabilities of Large Language Models (LLMs). The insights generated by the LLM are compiled in a Quant styled dataset and augmented by historical stock price data. A Machine Learning model is then trained with LLM outputs as features. The walkforward test results show promising outperformance wrt S&P500 returns. This paper intends to provide a framework for future work in this direction. To facilitate this, the code has been released as open source

Microkinetic Modeling of Complex Reaction Networks Using Automated Network Generation

University of Minnesota Ph.D. dissertation. April 2018. Major: Chemical Engineering. Advisors: Prodromos Daoutidis, Aditya Bhan. 1 computer file (PDF); xiv, 193 pages.Complex reaction networks are found in a variety of engineered and natural chemical systems ranging from petroleum processing to atmospheric chemistry and including biomass conversion, materials synthesis, metabolism, and biological degradation of chemicals. These systems comprise of several thousands of reactions and species interrelated through a highly interconnected network. These complex reaction networks can be constructed automatically from a small set of initial reactants and chemical transformation rules. Detailed kinetic modeling of these complex reaction systems is becoming increasingly important in the development, analysis, design, and control of chemical reaction processes. The key challenges faced in the development of a kinetic model for complex reaction systems include (1) multi-time scale behavior due to the presence of fast and slow reactions which introduces stiffness in the system, (2) lack of lumping schemes that scale well with the large size of the network, and (3) unavailability of accurate reaction rate constants (activation energies and pre-exponential factors). Model simplication and order reduction methods involving lumping, sensitivity analysis and time-scale analysis address the challenges of size and stiffness of the system. Although there exist numerical methods for simulation of large-scale, stiff models, the use of such models in optimization-based tasks (e.g. parameter estimation, control) results in ill-conditioning of the corresponding optimization task. This research presents methods, computational tools, and applications to address the two challenges that emerge in the development of microkinetic models of complex reaction networks in the context of chemical and biochemical conversion - (a) identifying the different time scales within the reaction system irrespective of the chemistry, and (b) identifying lumping and parameterization schemes to address the computational challenge of parameter estimation. The first question arises due to the presence of both fast and slow reactions simultaneously within the system. The second challenge is directly related to the estimation of the reaction rate constants that are unknown for these chemical reaction networks. Addressing these questions is a key step towards modeling, design, operation, and control of reactors involving complex systems. In this context, this thesis presents methods to address the computational challenges in developing microkinetic models for complex reaction networks. Rule Input Network Generator (RING), a network generation computational tool, is used for the network generation and analysis. First, the stiffness is addressed with the implementation of a graph-theoretic framework. Second, lumping and parameterization schemes are studied to address the size challenge of these reaction networks. A particular lumping and parameterization scheme is used to develop the microkinetic model for an olefin interconversion reaction system. Further, RING is extended for application of biochemical reaction network generation and analysis

Weightless: Lossy Weight Encoding For Deep Neural Network Compression

The large memory requirements of deep neural networks limit their deployment and adoption on many devices. Model compression methods effectively reduce the memory requirements of these models, usually through applying transformations such as weight pruning or quantization. In this paper, we present a novel scheme for lossy weight encoding which complements conventional compression techniques. The encoding is based on the Bloomier filter, a probabilistic data structure that can save space at the cost of introducing random errors. Leveraging the ability of neural networks to tolerate these imperfections and by re-training around the errors, the proposed technique, Weightless, can compress DNN weights by up to 496x with the same model accuracy. This results in up to a 1.51x improvement over the state-of-the-art

SERUM HOMOCYSTEINE AS A RISK FACTOR FOR STROKE: A PROSPECTIVE STUDY FROM A RURAL TERTIARY CARE CENTRE

Objective: Stroke is one of the leading causes of mortality and long-term disability in both developed and developing countries. Serum homocysteine level is one of the emerging modifiable risk factors for atherosclerosis which may result into a cerebrovascular accident. This study was designed to study the association of Serum Homocysteine level with the development of acute stroke at a rural tertiary care centre in North India.Methods: The present study was a prospective cross-sectional study conducted in the Department of Medicine, Maharishi Markandeshwar Institute of Medical Sciences and Research, Mullana, Ambala. The study population included 100 patients presenting with Stroke (either ischemic or hemorrhagic) in the indoor and outdoor facilities in the Department of Medicine. 50 age and sex-matched healthy individuals were taken as controls. Serum total Homocysteine level was measured in all the cases and controls.Results: Majority of the patients suffered from ischemic stroke (78%), while only 22% patients had hemorrhagic stroke. The mean Serum Homocysteine level in stroke patients (19.88±8.78 μmol/l) was significantly higher than in controls (10.48±4.39 μmol/l) (p<0.01). In a subgroup analysis, stroke patients with a positive history of smoking had significantly higher homocysteine level as compared to non-smokers (p<0.05).Conclusion: Increased level of Serum Homocysteine is significantly associated with risk of cerebrovascular accident, which is independent of the risk attributed to traditional risk factors.Â

Watching Stars in Pixels: The Interplay of Traffic Shaping and YouTube Streaming QoE over GEO Satellite Networks

Geosynchronous satellite (GEO) networks are a crucial option for users beyond terrestrial connectivity. However, unlike terrestrial networks, GEO networks exhibit high latency and deploy TCP proxies and traffic shapers. The deployment of proxies effectively mitigates the impact of high network latency in GEO networks, while traffic shapers help realize customer-controlled data-saver options that optimize data usage. It is unclear how the interplay between GEO networks' high latency, TCP proxies, and traffic-shaping policies affects the quality of experience (QoE) for commonly used video applications. To fill this gap, we analyze the quality of over $2$k YouTube video sessions streamed across a production GEO network with a $900$Kbps shaping rate. Given the average bit rates for the selected videos, we expected seamless streaming at $360$p or lower resolutions. However, our analysis reveals that this is not the case: $28\%$ of TCP sessions and $18\%$ of gQUIC sessions experience rebuffering events, while the median average resolution is only $380$p for TCP and $299$p for gQUIC. Our analysis identifies two key factors contributing to sub-optimal performance: (i)unlike TCP, gQUIC only utilizes $63\%$ of network capacity; and (ii)YouTube's imperfect chunk request pipelining. As a result of our study, the partner GEO ISP discontinued support for the low-bandwidth data-saving option in U.S. business and residential markets to avoid potential degradation of video quality -- highlighting the practical significance of our findings

Medical student’s perception and feed-back on virtual classes during COVID-19 pandemic: a multi-centric questionnaire based study

Introduction: The quick turn to online platforms from contact learning during COVID-19 remained challenging for both teachers as well for students. This study was done with the aim to know the perception and feed back of under-graduate medical students on virtual classes during the pandemic. Material & Methods: This was a cross-sectional questionnaire based multi-centric study.  Questionnaire in the form of Google form was distributed to the undergraduate medical students from various MBBS professionals studying in various medical colleges across North India. The completed questionnaire was collected and data was analyzed. Results: 40% students were from government, 52% from private medical colleges and 8% from AIIMS/ SGPGI. Majority of students were using mobile (63.7%) for e learning, using 4G internet (70.6%). Mostly the private medical colleges (73%) and only 4.5% government colleges were conducting the live video classes. Rest of them was providing the soft copy of the study material to the students. Based on the feedback by the students, about one third of the students (36.7%) appreciated the online platform in the current scenario as well for future in the combination with traditional classroom teaching. Discussion: The e-learning was the need of the hour as every day is important for a medical student and the learning has to be uninterrupted. Although helpful, e-learning alone is a far cry from face‐to‐face interaction between students and teachers. Finding the right balance of class-room teaching combined with e-learning should become the norm for future students.   &nbsp