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Zygosporium Mont.: an important saprophyte element on monocot leaves in Himachal Himalayan Siwalik (Late Miocene) forest ecosystem
No full textAlthough modern members of Zygosporium are reported on a wide range of monocotyledons, the fossil record of this hyphomycetous fungus on monocots is rather poor. Here, we document the occurrence of Zygosporium fossils in appreciable numbers on the cuticular fragments of compressed monocot leaves preserved in Late Miocene Middle Siwalik sediments of Himachal Pradesh, western Himalaya. Based on preserved conidiophore micromorphology (vesicular conidiophores with dark-pigmented, curved vesicular cells and cylindrical stalk cells produced from the side of the septate setiform conidiophores), the Siwalik fungal remains are confidently placed within the modern anamorphic genus Zygosporium Mont. (Zygosporiaceae: Xylariales: Sordariomycetes). However, we confine their taxonomic assignment up to a generic level and so, designate them here as morphospecies. Abundant Zygosporium fossils indicate that Zygosporium was widely diversified and distributed as a saprophyte element on monocot leaves in Himachal Himalaya during the Siwalik time (Late Miocene, ca.12–8 Ma). As modern representatives of Zygosporium are predominantly saprotrophs and prefer a warm tropical climate, we suggest that the current Zygosporium fossils favored a warm tropical climate and were saprophytes on fallen, decaying monocot leaves during the time of deposition. In addition, this report provides valuable information on the diversification, evolution, and ecology of this anamorphic fungus in India during the Siwalik time
Agent-Based Triangle Counting and its Applications in Anonymous Graphs
No full textTriangle counting in graphs is a fundamental problem with a diverse application domain. In this paper, we propose a solution to the triangle counting problem in an anonymous graph using autonomous mobile agents. We further use the triangle count to address the Truss Decomposition problem which involves finding maximal sub-graphs with strong interconnections. Truss decomposition helps in identifying maximal, highly interconnected sub-graphs, or trusses, within a network. Additionally, the triangle count is also used to compute two important metrics - Triangle Centrality and Local Clustering Coefficient for the nodes of the graph. Our goal is to devise algorithms that effectively solve these problems minimizing both the overall time complexity and the memory usage at each agent
Aggregate Encryption Individual Decryption for FPGA Bitstream Protection on Cloud
No full textCloud computing platforms are progressively adopting Field Programmable Gate Arrays (FPGAs) to deploy specialized hardware accelerators for specific computational tasks. However, the security of FPGA-based bitstream for Intellectual Property (IP) cores from unauthorized interception in cloud environments remains a prominent concern. Existing methodologies for protection of these bitstreams have several limitations, such as requiring a large number of keys, tying bitstreams to specific FPGAs, and relying on trusted third parties. This paper proposes AgEID (Aggregate Encryption and Individual Decryption), a cryptosystem based on key aggregation to enhance the security of FPGA-based bitstreams for IP cores and to address the pitfalls of previous related works. By this scheme, IP providers can encrypt their bitstreams using a single key for a given set of FPGA boards, and this same key then decrypts the bitstream on any of the FPGA boards within that set. Aggregate encryption of this single key is performed in a way which ensures that the key can solely be obtained onboard through individual decryption employing the board\u27s private key, thus facilitating secure key provisioning, The proposed cryptosystem is evaluated mainly on Xilinx Zynq™7000 FPGAs. The outcomes demonstrate that our cryptosystem outperforms existing techniques with respect to resource, time and energy sianiflcantly
An Innovative Way to Increase the Speed and Efficiency of Reverse Mapping of Elliptic Curve Points
No full textElliptic curve cryptography is a widely recognized method of using a public key cryptosystem for encrypting and decrypting data. The message must be transformed to points on an Elliptic Curve using a mapping algorithm in order to send the message. This process of mapping the message to the point on the elliptic curve is done by applying a well-known mapping algorithm. Elliptic curve cryptography is widely acknowledged to have simpler mathematical operations compared to other cryptographic methods. However, researchers in the field of cryptography have faced challenges in mapping plain messages onto points on an elliptic curve and reversing the process at the receiver\u27s end for decryption. Recent advancements have led to the development of mapping techniques that are resistant to frequency analysis. In this paper, we have discussed a well-known mapping scheme that has been used to map plain text messages to points on elliptic curves. However, the time taken to map a message and encrypt the points takes a substantial amount of time. This latency can be reduced if we parallelize the mapping process. In this paper, we have parallelized the reverse mapping phase of the scheme at the receiver\u27s end to reduce the total communication time. Performance results after parallelization have been recorded and provided in the later section of the paper in tabular and graphical form
Autoencoder and Extreme Learning Machine Based Deep Multi-label Classifier
No full textIn this article, a novel deep neural network model has been proposed for multi-label (ML) classification. Deep architectures, well-known for their information extraction and learning capabilities, have been specifically considered here to deal with the complex nature of ML data. The proposed model broadly has two phases: feature extraction and cascaded ML classification. In the first phase, deep autoencoders (DAEs) have been employed to handle the large feature space of ML data. The subsequent phase of the network takes these reduced and enhanced features and passes them through a cascade of ML extreme learning machines (MLELMs) which intricately learns the input to output mapping and performs ML classification. This proposed stacked network is capable of handling a large feature space while performing fast classification. Experiments have been done with benchmark ML datasets by varying the size of the network components to determine the optimal depth of the proposed model. Analysis of DAE vs stacked autoencoder (SAE) has also been done for best feature extraction. Comparison with six state-of-the-art classifiers also shows the proposed model to have superior performance in most cases
CateReR: A Graph Neural Network-Based Model for Category-Wise Reliability-Aware Recommendation
No full textIn recent years of Web, it has been an interesting research topic to recommend items/objects based on the user’s choice. Due to the exchange of opinions about items over the social network, trust plays a crucial role in liking or disliking an item. Although the Graph Neural Networks (GNNs), with their natural ability to integrate node information and topological structure, have shown enormous potential in the trust-aware social recommendation, these do not implicitly deal with external factors, such as ‘item category’, that may have a remarkable impact on user-trust. In this paper, we present a novel approach that project trust as dependent on the category of product. Subsequently, we augment GNN-based social recommendation by defining a concept of category-based user-reliability value. Our proposed graph neural network-based model for category-wise reliability-aware recommendation (CateReR) finds user-embedding and item-embedding with consideration to the variation of user’s reliability over different product categories. CateReR is also capable of dealing with trust propagation and trust composition, which are often ignored by existing GNN-based models. We have experimented with CateReR on two real-life datasets to show the usefulness of the model
Deterministic Way of Designing Sensing Matrix
No full textWith all the scientific and technological advancements, signal reconstruction is still a challenging problem in many ways. Mathematically, signal reconstruction is a linear map from the Hilbert space of square-integrable function LP to complex space Cn. We assume there can be any sampling method and a reconstruction formula for such a linear map. In principle, it is possible first to choose a reconstruction formula and then either compute some sampling algorithm based on the chosen reconstruction formula or analyse the behaviour of a given sampling algorithm concerning the given formula. In 1948, Claude Shannon\u27s groundwork on the sampling theorem was published, which became the backbone for almost all types of digital signal processing techniques. This famous Nyquist - Shannon Sampling theorem follows a strict sufficient condition for the perfect reconstruction of the sampled signal. In 2004, the concept of compressive sensing was introduced, which uses the given knowledge of sparsity and can reconstruct the signal perfectly even with fewer samples compared to the samples required according to the sampling theorem. In compressive sensing, one measure of difficulty is designing the sensing matrix. Random matrices with Gaussian, Bernoulli, and Fourier entries are highly used as sensing matrices in compressive sensing for signal recovery. However, random matrices are not suitable for every application. In this paper, we provide a new deterministic way of designing a sensing matrix, provided we incorporate prior knowledge about signal acquisition. At the last, we will show the reconstruction result by using the proposed sensing matrix
Growth Rate of the Number of Empty Triangles in the Plane
No full textGiven a set P of n points in the plane, in general position, denote by NΔ(P) the number of empty triangles with vertices in P. In this paper we investigate by how much NΔ(P) changes if a point x is removed from P. By constructing a graph GP(x) based on the arrangement of the empty triangles incident on x, we transform this geometric problem to the problem of counting triangles in the graph GP(x). We study properties of the graph GP(x) and, in particular, show that it is kite-free. This relates the growth rate of the number of empty triangles to the famous Ruzsa-Szemerédi problem
Maintenance Process Modelling for Systems Subjected to Multiple Failure Mechanisms and Maintenance Actions
No full textRepairable systems subjected to multiple dependent failure mechanisms are considered. The first objective of the paper is to propose a global framework for the modelling of their maintenance process. A system can be subjected to planned preventive, condition-based and corrective maintenance operations. Maintenance actions restoring the system from better-than-new to worse-than-minimal are considered. The stochastic modelling takes into account any kind of dependency between failure mechanisms. The second objective is to present new modellings of failure mechanisms and maintenance efficiencies while presenting a review of the most well-known assumptions in maintenance analysis. The remaining useful life of the system can be computed. In a statistical approach, the likelihood function can be derived. A comprehensive method to build an entirely specified parametric model is discussed, considering either one kind or multiple kinds of maintenance actions. An application to real data is finally presented
Towards Standardizing the Library Circulation Metadata
No full textThe main purpose of the study is to provide metadata vocabulary (MV) for easy sharing, migration, and analysis of library circulation transaction (LCT) data across the libraries and library management software (LMS) to facilitate evidence-based decision-making and improved library user services. In the context of a library, the practice of using standard MV (e.g., MARC21) for describing bibliographic data is very prominent. We find various initiatives, like BIBFRAME by the Library of Congress which is grounded in linked data techniques for the future of bibliographic data on the Web and broadly in the network world. However, when it comes to managing other kinds of data, e.g., circulation and acquisition transaction data, we do not find any single vocabulary or initiative to refer to. We find the usage of different metadata elements for describing and capturing similar data across the various LMS tools. This leads to several issues and their consequences as are documented in this paper. These issues necessities the development of a metadata vocabulary for circulation transaction data (CTD) which can be referred to and used universally across the LMS. The study is a step towards the development of a reference MV for effective and efficient management of CTD in the library field