ElfStore: A Resilient Data Storage Service for Federated Edge and Fog Resources

Edge and fog computing have grown popular as IoT deployments become wide-spread. While application composition and scheduling on such resources are being explored, there exists a gap in a distributed data storage service on the edge and fog layer, instead depending solely on the cloud for data persistence. Such a service should reliably store and manage data on fog and edge devices, even in the presence of failures, and offer transparent discovery and access to data for use by edge computing applications. Here, we present Elfstore, a first-of-its-kind edge-local federated store for streams of data blocks. It uses reliable fog devices as a super-peer overlay to monitor the edge resources, offers federated metadata indexing using Bloom filters, locates data within 2-hops, and maintains approximate global statistics about the reliability and storage capacity of edges. Edges host the actual data blocks, and we use a unique differential replication scheme to select edges on which to replicate blocks, to guarantee a minimum reliability and to balance storage utilization. Our experiments on two IoT virtual deployments with 20 and 272 devices show that ElfStore has low overheads, is bound only by the network bandwidth, has scalable performance, and offers tunable resilience.Comment: 24 pages, 14 figures, To appear in IEEE International Conference on Web Services (ICWS), Milan, Italy, 201

Quantum scattering cross sections of O(3P^3P) + N2_2 collisions for planetary aeronomy

"Hot atoms", which are atoms in their excited states, transfer their energy to the surrounding atmosphere through collisions. This process of energy transfer is known as thermalization, and it plays a crucial role in various astrophysical and atmospheric processes. Thermalization of hot atoms is mainly governed by the amount of species present in the surrounding atmosphere and the collision cross-section between the hot atoms and surrounding species. In this work, we investigated the elastic and inelastic collisions between hot oxygen atoms and neutral N$_2$ molecules, relevant to oxygen gas escape from the martian atmosphere and for characterizing the chemical reactions in hypersonic flows. We conducted a series of quantum scattering calculations between various isotopes of O($^3P$) atoms and N$_2$ molecules across a range of collision energies (0.3 to 4 eV), and computed both their differential and collision cross-sections using quantum time$-$independent coupled-channel approach. Our differential cross-section results indicate a strong preference for forward scattering over sideways or backward scattering, and this anisotropy in scattering is further pronounced at higher collision energies. By comparing the cross-sections of three oxygen isotopes, we find that the heavier isotopes consistently have larger collision cross-sections than the lighter isotopes over the entire collision energy range examined. However, for all the isotopes, the variation of collision cross-section with respect to collision energy is the same. As a whole, the present study contributes to a better understanding of the energy distribution and thermalization processes of hot atoms within atmospheric environments. Specifically, the cross$-$sectional data presented in this work is directly useful in improving the accuracy of energy relaxation modeling of O and N$_2$ collisions over Mars and Venus atmospheres.Comment: 7 pages, 5 figures, 4 tables, submitted to MNRA

Green Approach for Next Generation Computing: A Survey

In the past few years, the Information and Communication Technology sector have made a significant advancement in Cloud Computing technology. The world wide acceptance of Cloud technology can be credited to the various benefits a Cloud offers to its users. The Cloud technology helps in preventing resource wastage to a much greater extent. The authors of this paper wish to explore the concept behind cloud computing, its strategy, and benefits. This paper presents some facts and figures related to cloud and green computing which will help in obtaining the gist of Cloud Computing and in understanding the need of going green in computing

Emission Factors for Continuous Fixed Chimney Bull Trench Brick Kiln (FCBTK) in India

Uncertainty in emissions from brick manufacturing is a major concern and more primary monitoring based datasets are required. This study presents latest emission factors for continuous fixed chimney bull trench brick kilns (FCBTK), which is the main technology used for brick production in India. Stack monitoring of kilns in a typical brick manufacturing cluster in India is carried out to monitor emissions of pollutants like PM, SO2 and CO. Average concentrations of PM, SO2 and CO in the stacks are measured to be 172±76, 114±47 and 484±198 mg/Nm3, respectively. Monitored stack concentrations are used to compute emission factors based on brick production and fuel consumption activities in the cluster. The computed emission factors across different kilns ranged between 0.81-1.18, 0.57-0.71 and 2.07-2.80g/kg of fired bricks for PM, SO2 and CO, respectively. Corresponding emission factors per unit of coal used in brick kilns are found to be in the range of 13-29, 9-15, 40-56 g /kg for PM, SO2 and CO, respectively. The differences in emission factors are mainly due to variations in the quality of coal used by different kilns. Good correlations were observed between changing calorific values, ash and sulphur content of coal and emissions monitored in the kilns. These new factors can be used for improvement in emission inventories and thereafter modelling results for the region

Balanced Deep CCA for Bird Vocalization Detection

Event detection improves when events are captured by two different modalities rather than just one. But to train detection systems on multiple modalities is challenging, in particular when there is abundance of unlabelled data but limited amounts of labeled data. We develop a novel self-supervised learning technique for multi- modal data that learns (hidden) correlations between simultaneously recorded microphone (sound) signals and accelerometer (body vibration) signals. The key objective of this work is to learn useful embeddings associated with high performance in downstream event detection tasks when labeled data is scarce and the audio events of interest — songbird vocalizations — are sparse. We base our approach on deep canonical correlation analysis (DCCA) that suffers from event sparseness. We overcome the sparseness of positive labels by first learning a data sampling model from the labelled data and by applying DCCA on the output it produces. This method that we term balanced DCCA (b-DCCA) improves the performance of the unsupervised embeddings on the down-stream supervised audio detection task compared to classsical DCCA. Because data labels are frequently imbalanced, our method might be of broad utility in low-resource scenarios

Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection

This paper introduces WhisperSeg, utilizing the Whisper Transformer pre-trained for Automatic Speech Recognition (ASR) for human and animal Voice Activity Detection (VAD). Contrary to traditional methods that detect human voice or animal vocalizations from a short audio frame and rely on careful threshold selection, WhisperSeg processes entire spectrograms of long audio and generates plain text representations of onset, offset, and type of voice activity. Processing a longer audio context with a larger network greatly improves detection accuracy from few labeled examples. We further demonstrate a positive transfer of detection performance to new animal species, making our approach viable in the data-scarce multi-species setting.$^{1}

Role of live microbial feed supplements with reference to anaerobic fungi in ruminant productivity: A review

To keep the concept of a safe food supply to the consumers, animal feed industries world over are showing an increasing interest in the direct-fed microbials (DFM) for improved animal performance in terms of growth or productivity. This becomes all the more essential in a situation, where a number of the residues of antibiotics and/or other growth stimulants reach in milk and meat with a number of associated potential risks for the consumers. Hence, in the absence of growth stimulants, a positive manipulation of the rumen microbial ecosystem to enhance the feedstuff utilization for improved production efficiency by ruminants has become of much interest to the researchers and entrepreneurs. A few genera of live microbes (i.e., bacteria, fungi and yeasts in different types of formulations from paste to powder) are infrequently used as DFM for the domestic ruminants. These DFM products are live microbial feed supplements containing naturally occurring microbes in the rumen. Among different DFM possibilities, anaerobic rumen fungi (ARF) based additives have been found to improve ruminant productivity consistently during feeding trials. Administration of ARF during the few trials conducted, led to the increased weight gain, milk production, and total tract digestibility of feed components in ruminants. Anaerobic fungi in the rumen display very strong cell-wall degrading cellulolytic and xylanolytic activities through rhizoid development, resulting in the physical disruption of feed structure paving the way for bacterial action. Significant improvements in the fiber digestibility were found to coincide with increases in ARF in the rumen indicating their role. Most of the researches based on DFM have indicated a positive response in nutrient digestion and methane reducing potential during in vivo and/or in vitro supplementation of ARF as DFM. Therefore, DFM especially ARF will gain popularity but it is necessary that all the strain