A SERVER HARDENING FRAMEWORK

There have been several attempts at  improving the security of servers in all the fields be it web servers like apache tomcat ,mail servers like wamp etc. Checklists have been made for different servers from time to time which contains a list of steps that have to be followed in order to improve the security of the particular server. So the user has to have all the basic knowledge about the server before he can make use of the checklist and secure the server. This is the first problem that the user has to be well versed in the basic technicalities of the server configuration before he can secure it for use. Secondly ,till now there is no tool or framework that can bring all the different types of servers together under it so that a single framework can be used to harden or secure multiple number of servers and without any knowledge about the basic configuration of the servers. Hence, we propose to automate the server hardening process by creating a Framework which will be open source and hence new servers could be included in it by users by editing the open source code of the framework which would be in python language. A server hardening framework would help even a person with a layman understanding to secure the server which he is using. He would be able to use the framework for hardening a multiple types of servers as per his requirements. The Framework will provide an option of AUDITING as well as HARDENING. If the User chooses the AUDITING option , then the parameters of the server configuration file would be displayed along with the current values as well as it would be mentioned additionally for the parameters if a particular parameter requires hardening and again the user would be asked if he wants to harden it or not. In case of choosing hardening, the server configuration file would be replaced by hardened file and server be restarted

Assessment of Seasonal and Site-Speci�c Variations in Soil Physical,Chemical and Biological Properties Around Opencast Coal Mines

Coal mining adversely affects soil quality around opencast mines. Therefore, a study was conducted in 2010 and 2011 to assess seasonal and site-specific variations in physical, chemical, and biological properties of soil collected at different distances from mining areas in the Jharia coalfield, India. Throughout the year, the soil in sites near coal mines had a significantly higher bulk density, temperature, electrical conductivity, and sulfate and heavy metal contents and a significantly lower water-holding capacity, porosity, moisture content, pH, and total nitrogen and available phosphorus contents, compared with the soil collected far from the mines. However, biological properties were site-specific and seasonal. Soil microbial biomass carbon (MBC) and nitrogen (MBN), MBC/MBN,and soil respiration were the highest during the rainy season and the lowest in summer, with the minimum values in the soil near coal mines. A soil quality index revealed a significant effect of heavy metal content on soil biological properties in the coal mining areas

AxoNN: An asynchronous, message-driven parallel framework for extreme-scale deep learning

In the last few years, the memory requirements to train state-of-the-art neural networks have far exceeded the DRAM capacities of modern hardware accelerators. This has necessitated the development of efficient algorithms to train these neural networks in parallel on large-scale GPU-based clusters. Since computation is relatively inexpensive on modern GPUs, designing and implementing extremely efficient communication in these parallel training algorithms is critical for extracting the maximum performance. This paper presents AxoNN, a parallel deep learning framework that exploits asynchrony and message-driven execution to schedule neural network operations on each GPU, thereby reducing GPU idle time and maximizing hardware efficiency. By using the CPU memory as a scratch space for offloading data periodically during training, AxoNN is able to reduce GPU memory consumption by four times. This allows us to increase the number of parameters per GPU by four times, thus reducing the amount of communication and increasing performance by over 13%. When tested against large transformer models with 12-100 billion parameters on 48-384 NVIDIA Tesla V100 GPUs, AxoNN achieves a per-GPU throughput of 49.4-54.78% of theoretical peak and reduces the training time by 22-37 days (15-25% speedup) as compared to the state-of-the-art.Comment: Proceedings of the IEEE International Parallel & Distributed Processing Symposium (IPDPS). IEEE Computer Society, May 202

Explosive Remnants of War: A War after the War?

Explosive Remnants of War (ERW) pose significant humanitarian problems to the civilians as well as to the governments in post conflict situations. People continue to be at risk even after the war due to the presence of ERW. The issue of ERW has in fact shifted the focus of the international community from the immediate impacts of the weapons to their long term effects. In response to this, states concluded a landmark agreement, Protocol V to the UN Convention on Certain Conventional Weapons in 2003 (CCW). This Protocol aims at providing a proper mechanism to deal with ERW threat. Meanwhile, with the beginning of the new century and the emergence of newly sophisticated weapons the debate over the ERW got shifted to one of the most menacing category of weapons called cluster munitions. Again, responding to the problem, the state parties adopted the Convention of Cluster Munitions 2003 which bans the use and development of these deadly weapons. Both these instruments suffer from certain inherent limitations. Despite these limitations they still serve as the last resort for the civilians as well as for the governments of the war torn communities in dealing with the catastrophic effects of ERW

Entanglement on linked boundaries in Chern-Simons theory with generic gauge groups

We study the entanglement for a state on linked torus boundaries in $3d$ Chern-Simons theory with a generic gauge group and present the asymptotic bounds of R\'enyi entropy at two different limits: (i) large Chern-Simons coupling $k$, and (ii) large rank $r$ of the gauge group. These results show that the R\'enyi entropies cannot diverge faster than $\ln k$ and $\ln r$, respectively. We focus on torus links $T(2,2n)$ with topological linking number $n$. The R\'enyi entropy for these links shows a periodic structure in $n$ and vanishes whenever $n = 0 \text{ (mod } \textsf{p})$, where the integer $\textsf{p}$ is a function of coupling $k$ and rank $r$. We highlight that the refined Chern-Simons link invariants can remove such a periodic structure in $n$.Comment: 31 pages, 5 figure

Śāntarakṣita and Kamalaśīla on the Advaita Vedanta Theory of a Self

In this article we assess Śāntarakṣita’s and Kamalaśīla’s critique of the Advaita Vedānta theory of self. We provide a translation of the verses 328-335 of the commentary titled Tattvasaṃgrahapañjikā, which was composed by Kamalaśīla on Śāntarakṣita’s Tattvasaṃgraha. We present Śāntarakṣita’s and Kamalaśīla’s views of a self and also explain the Advaita Vedānta theory based on the texts of Śaṅkara. It is concluded in the article that Śāntarakṣita and Kamalaśīla failed to consider the most likely Advaitin replies to their objections, especially the reply that cognitions of objects are illusory rather than real modifications, since the critique assumed that they were real modifications

Jorge: Approximate Preconditioning for GPU-efficient Second-order Optimization

Despite their better convergence properties compared to first-order optimizers, second-order optimizers for deep learning have been less popular due to their significant computational costs. The primary efficiency bottleneck in such optimizers is matrix inverse calculations in the preconditioning step, which are expensive to compute on GPUs. In this paper, we introduce Jorge, a second-order optimizer that promises the best of both worlds -- rapid convergence benefits of second-order methods, and high computational efficiency typical of first-order methods. We address the primary computational bottleneck of computing matrix inverses by completely eliminating them using an approximation of the preconditioner computation. This makes Jorge extremely efficient on GPUs in terms of wall-clock time. Further, we describe an approach to determine Jorge's hyperparameters directly from a well-tuned SGD baseline, thereby significantly minimizing tuning efforts. Our empirical evaluations demonstrate the distinct advantages of using Jorge, outperforming state-of-the-art optimizers such as SGD, AdamW, and Shampoo across multiple deep learning models, both in terms of sample efficiency and wall-clock time