Ciphered BCH Codes for PAPR Reduction in the OFDM in Underwater Acoustic Channels

We propose an effective, low complexity and multifaceted scheme for peak-to-average power ratio (PAPR) reduction in the orthogonal frequency division multiplexing (OFDM) system for underwater acoustic (UWA) channels. In UWA OFDM systems, PAPR reduction is a challenging task due to low bandwidth availability along with computational and power limitations. The proposed scheme takes advantage of XOR ciphering and generates ciphered Bose–Chaudhuri–Hocquenghem (BCH) codes that have low PAPR. This scheme is based upon an algorithm that computes several keys offline, such that when the BCH codes are XOR-ciphered with these keys, it lowers the PAPR of BCH-encoded signals. The subsequent low PAPR modified BCH codes produced using the chosen keys are used in transmission. This technique is ideal for UWA systems as it does not require additional computational power at the transceiver during live transmission. The advantage of the proposed scheme is threefold. First, it reduces the PAPR; second, since it uses BCH codes, the bit error rate (BER) of the system improves; and third, a level of encryption is introduced via XOR ciphering, enabling secure communication. Simulations were performed in a realistic UWA channel, and the results demonstrated that the proposed scheme could indeed achieve all three objectives with minimum computational power.

Ensemble Averaging of Transfer Learning Models for Identification of Nutritional Deficiency in Rice Plant

Computer vision-based automation has become popular in detecting and monitoring plants’ nutrient deficiencies in recent times. The predictive model developed by various researchers were so designed that it can be used in an embedded system, keeping in mind the availability of computational resources. Nevertheless, the enormous popularity of smart phone technology has opened the door of opportunity to common farmers to have access to high computing resources. To facilitate smart phone users, this study proposes a framework of hosting high end systems in the cloud where processing can be done, and farmers can interact with the cloud-based system. With the availability of high computational power, many studies have been focused on applying convolutional Neural Networks-based Deep Learning (CNN-based DL) architectures, including Transfer learning (TL) models on agricultural research. Ensembling of various TL architectures has the potential to improve the performance of predictive models by a great extent. In this work, six TL architectures viz. InceptionV3, ResNet152V2, Xception, DenseNet201, InceptionResNetV2, and VGG19 are considered, and their various ensemble models are used to carry out the task of deficiency diagnosis in rice plants. Two publicly available datasets from Mendeley and Kaggle are used in this study. The ensemble-based architecture enhanced the highest classification accuracy to 100% from 99.17% in the Mendeley dataset, while for the Kaggle dataset; it was enhanced to 92% from 90%.

Distributing Epistemic Functions and Tasks - A Framework for Augmenting Human Analytic Power With Machine Learning in Science Education Research

Machine learning has become commonplace in educational research and science education research, especially to support assessment efforts. Such applications of machine learning have shown their promise in replicating and scaling human-driven codes of students’ work. Despite this promise, we and other scholars argue that machine learning has not achieved its transformational potential. We argue that this is because our field is currently lacking frameworks for supporting creative, principled, and critical endeavors to use machine learning in science education research. To offer considerations for science education researchers’ use of ML, we present a framework, Distributing Epistemic Functions and Tasks (DEFT), that highlights the functions and tasks that pertain to generating knowledge that can be carried out by either trained researchers or machine learning algorithms. Such considerations are critical decisions that should occur alongside those about, for instance, the type of data or algorithm used. We apply this framework to two cases, one that exemplifies the cutting-edge use of machine learning in science education research and another that offers a wholly different means of using machine learning and human-driven inquiry together. We conclude with strategies for researchers to adopt machine learning and call for the field to rethink how we prepare science education researchers in an era of great advances in computational power and access to machine learning methods.

Comparing Deep Neural Networks and Gradient Boosting for Pneumonia Detection Using Chest X-Rays

In recent years, with the development of computational power and the explosion of data available for analysis, deep neural networks, particularly convolutional neural networks, have emerged as one of the default models for image classification, outperforming most of the classical machine learning models in this task. On the other hand, gradient boosting, a classical model, has been widely used for tabular structure data and leading data competitions, such as those from Kaggle. In this study, the authors compare the performance of deep neural networks with gradient boosting models for detecting pneumonia using chest x-rays. The authors implement several popular architectures of deep neural networks, such as Resnet50, InceptionV3, Xception, and MobileNetV3, and variants of a gradient boosting model. The authors then evaluate these two classes of models in terms of prediction accuracy. The computation in this study is done using cloud computing services offered by Google Colab Pro.

Designing an Efficient and Highly Dynamic Substitution-Box Generator for Block Ciphers Based on Finite Elliptic Curves

Developing a substitution-box (S-box) generator that can efficiently generate a highly dynamic S-box with good cryptographic properties is a hot topic in the field of cryptography. Recently, elliptic curve (EC)-based S-box generators have shown promising results. However, these generators use large ECs to generate highly dynamic S-boxes and thus may not be suitable for lightweight cryptography, where the computational power is limited. The aim of this paper is to develop and implement such an S-box generator that can be used in lightweight cryptography and perform better in terms of computation time and security resistance than recently designed S-box generators. To achieve this goal, we use ordered ECs of small size and binary sequences to generate certain sequences of integers which are then used to generate S-boxes. We performed several standard analyses to test the efficiency of the proposed generator. On an average, the proposed generator can generate an S-box in 0.003 seconds, and from 20,000 S-boxes generated by the proposed generator, 93 % S-boxes have at least the nonlinearity 96. The linear approximation probability of 1000 S-boxes that have the best nonlinearity is in the range [0.117, 0.172] and more than 99% S-boxes have algebraic complexity at least 251. All these S-boxes have the differential approximation probability value in the interval [0.039, 0.063]. Computational results and comparisons suggest that our newly developed generator takes less running time and has high security against modern attacks as compared to several existing well-known generators, and hence, our generator is suitable for lightweight cryptography. Furthermore, the usage of binary sequences in our generator allows generating plaintext-dependent S-boxes which is crucial to resist chosen-plaintext attacks.

Multipoint-Interconnected Quantum Communication Networks

As quantum computers with sufficient computational power are becoming mature, the security of classical communication and cryptography may compromise, which is based on the mathematical complexity. Quantum communication technology is a promising solution to secure communication based on quantum mechanics. To meet the secure communication requirements of multiple users, multipoint-interconnected quantum communication networks are specified, including quantum key distribution networks and quantum teleportation networks. The enabling technologies for quantum communication are the important bases for multipoint-interconnected quantum communication networks. To achieve the better connection, resource utilization, and resilience of multipoint-interconnected quantum communication networks, the efficient network architecture and optimization methods are summarized, and open issues in quantum communication networks are discussed.

Cortical Cartography: Mapping Arealization Using Single-Cell Omics Technology

The cerebral cortex derives its cognitive power from a modular network of specialized areas processing a multitude of information. The assembly and organization of these regions is vital for human behavior and perception, as evidenced by the prevalence of area-specific phenotypes that manifest in neurodevelopmental and psychiatric disorders. Generations of scientists have examined the architecture of the human cortex, but efforts to capture the gene networks which drive arealization have been hampered by the lack of tractable models of human neurodevelopment. Advancements in “omics” technologies, imaging, and computational power have enabled exciting breakthroughs into the molecular and structural characteristics of cortical areas, including transcriptomic, epigenomic, metabolomic, and proteomic profiles of mammalian models. Here we review the single-omics atlases that have shaped our current understanding of cortical areas, and their potential to fuel a new era of multi-omic single-cell endeavors to interrogate both the developing and adult human cortex.

Patches in a timeline with ossia score

Handling of time and scores in patchers such as PureData, Max/MSP has been an ongoing concern for composers and users of such software. We introduce an integration of PureData inside the ossia score interactive and intermedia sequencer, based on libpd. This integration allows to score precisely event that are being sent to a PureData patch, and process the result of the patch’s computations afterwards in score. This paper describes the way this integration has been achieved, and how it enables composers to easily add a temporal dimension to a set of patches, by leveraging both the computational power of PureData and the temporal semantics of the ossia system, in order to create complex compositions.

Knowledge-based decision intelligence in street lighting management

As the availability of computational power and communication technologies increases, Humans and systems are able to tackle increasingly challenging decision problems. Taking decisions over incomplete visions of a situation is particularly challenging and calls for a set of intertwined skills that must be put into place under a clear rationale. This work addresses how to deliver autonomous decisions for the management of a public street lighting network, to optimize energy consumption without compromising light quality patterns. Our approach is grounded in an holistic methodology, combining semantic and Artificial Intelligence principles to define methods and artefacts for supporting decisions to be taken in the context of an incomplete domain. That is, a domain with absence of data and of explicit domain assertions.