Embracing the unknown in post-Bertalanffy systemics complexity modeling

Human beings' approach to the real world is about incompleteness: incompleteness of understanding, representation, information, etc. It focuses on the unknown, rather than on the production of mathematical certainties based on weak assumptions. The human brain is at least a factor of 1 billion more efficient than our present digital technology, and a factor of 10 million more efficient than the best digital technology that we can imagine. The unavoidable conclusion is that we have something fundamental to learn from the brain and biology about new ways and much more effective forms of computation and information managing. The presented approach, based on CICT, has shown to be quite helpful with high application flexibility. It can be applied at any system scale and open the door towards a more effective post-Bertalanffy Systemics Complexity modeling, taking into consideration system incompleteness, quasiness, and beyond

The Entropy Conundrum: A Solution Proposal

In 2004, physicist Mark Newman, along with biologist Michael Lachmann and computer scientist Cristopher Moore, showed that if electromagnetic radiation is used as a transmission medium, the most information-efficient format for a given 1-D signal is indistinguishable from blackbody radiation. Since many natural processes maximize the Gibbs-Boltzmann entropy, they should give rise to spectra indistinguishable from optimally efficient transmission. In 2008, computer scientist C.S. Calude and physicist K. Svozil proved that "Quantum Randomness" is not Turing computable. In 2013, academic scientist R.A. Fiorini confirmed Newman, Lachmann and Moore's result, creating analogous example for 2-D signal (image), as an application of CICT in pattern recognition and image analysis. Paradoxically if you don’t know the code used for the message you can’t tell the difference between an information-rich message and a random jumble of letters. This is an entropy conundrum to solve. Even the most sophisticated instrumentation system is completely unable to reliably discriminate so called "random noise" from any combinatorically optimized encoded message, which CICT called "deterministic noise". Entropy fundamental concept crosses so many scientific and research areas, but, unfortunately, even across so many different disciplines, scientists have not yet worked out a definitive solution to the fundamental problem of the logical relationship between human experience and knowledge extraction. So, both classic concept of entropy and system random noise should be revisited deeply at theoretical and operational level. A convenient CICT solution proposal will be presented

Quantum for 6G communication: a perspective

In the technologically changing world, the demand for ultra-reliable, faster, low power, and secure communication has significantly risen in recent years. Researchers have shown immense interest in emerging quantum computing (QC) due to its potentials of solving the computing complexity in the robust and efficient manner. It is envisioned that QC can act as critical enablers and strong catalysts to considerably reduce the computing complexities and boost the future of sixth generation (6G) and beyond communication systems in terms of their security. In this study, the fundamentals of QC, the evolution of quantum communication that encompasses a wide spectrum of technologies and applications and quantum key distribution, which is one of the most promising applications of quantum security, have been presented. Furthermore, various parameters and important techniques are also investigated to optimise the performance of 6G communication in terms of their security, computing, and communication efficiency. Towards the end, potential challenges that QC and quantum communication may face in 6G have been highlighted along with future directions

Linear and Non-Linear Synthesis of Unequally Spaced Time-Modulated Linear Arrays Using Evolutionary Algorithms

A novel method of designing unequally spaced time-modulated arrays (UESTMAs) by handling fewer optimization parameters with reduced problem dimension is presented in this paper. For synthesizing UESTMA, two design parameters, specifically, the non-linear parameter - element position, and the linear parameter – on-time durations are optimized in two steps. Different possible cases of linear and non-linear synthesis methods such as, position-only (PO), on-time only (OTO), position then on-time (PTOT), on-time then position (OTTP), and simultaneous position on-time (SPOT) are considered. To examine the performance of the synthesis methods, three global search stochastic algorithms based on differential evolution (DE), teaching-learning-based optimization (TLBO) and quantum particle swarm optimization (QPSO) have been employed to achieve the array pattern with significantly suppressed side lobe levels and sideband levels. Through comparative study, it is observed that the two step non-liner to linear synthesis method by fewer optimization parameters is efficient to provide better pattern with less computation time

Annual General Assembly of the International Association of Maritime Universities

978-84-947311-7-

Code-Switching with Word Senses for Pretraining in Neural Machine Translation

Lexical ambiguity is a significant and pervasive challenge in Neural Machine Translation (NMT), with many state-of-the-art (SOTA) NMT systems struggling to handle polysemous words (Campolungo et al., 2022). The same holds for the NMT pretraining paradigm of denoising synthetic "code-switched" text (Pan et al., 2021; Iyer et al., 2023), where word senses are ignored in the noising stage -- leading to harmful sense biases in the pretraining data that are subsequently inherited by the resulting models. In this work, we introduce Word Sense Pretraining for Neural Machine Translation (WSP-NMT) - an end-to-end approach for pretraining multilingual NMT models leveraging word sense-specific information from Knowledge Bases. Our experiments show significant improvements in overall translation quality. Then, we show the robustness of our approach to scale to various challenging data and resource-scarce scenarios and, finally, report fine-grained accuracy improvements on the DiBiMT disambiguation benchmark. Our studies yield interesting and novel insights into the merits and challenges of integrating word sense information and structured knowledge in multilingual pretraining for NMT.Comment: EMNLP (Findings) 2023 Long Pape

Exploring the information processing capabilities of random dendritic neural nets

The goal of this research is to investigate to what degree randona artificial dendritic nets can differentiate between temporal patterns after modifying the synaptic weights of certain synapses according to a learning algorithm based on the Fourier transform. A dendritic net is organized into subnets, which provide impulse responses to a function as a basis for Fourier decomposition of the input pattern. Each subnet is randomly generated. According to the simulations, randomly generated subnets with appropriate parameters are good enough to provide the impulse responses for the Fourier decomposition. The electrical potential pattern across the membrane of the dendrites follows the cable equation. The simulations use a linear synapse model, which is an approximation to biologically realistic synapses. Both excitatory and inhibitory synapses are present in a dendritic net. The simulations show that random dendritic nets with a small number of subnets can be modified to differentiate between electrical current patterns to a high degree when the membrane conductance of the dendrites is high, and they also show that the random structures are highly fault-tolerant. The performance of a random dendritic net does not change much after adding or deleting subnets

Investigating internet of things impact on e-Learning system: An overview

e-Learning systems have reached their peak with the revolution of smart technologies. In the past few years, the Internet of Things (IoT) has become one of the most advanced and popular technologies, affecting many different areas. Using IoT in an e-learning system is a fantastic technology that improves the e-learning system and makes it more inventive and cutting-edge. The key challenge addressed in this study is the acceptance of IoT usage in e-learning systems as well as how to improve it so that it can be utilized properly. This research concentrates on how IoT can benefit e-learning systems and how it might benefit users of e-learning systems. A comprehensive literature review was conducted to get acquainted with the important research related to IoT technology and e-learning systems through online research databases and reliable scientific journals. The first research finding is that e-learning systems need such modern techniques as IoT to enable interconnection, increase reliability, and enhance the enjoyment of the educational process. The second result is that research related to the development of new technologies like the IoT has a significant impact on enhancing the performance of new systems and bringing about positive change. This study highlights the value of IoT, particularly in e-learning systems. It aids in the development of new strategies that will improve the efficacy of e-learning systems and stimulate researchers to develop advanced technology

Cluster Heads Selection and Cooperative Nodes Selection for Cluster-based Internet of Things Networks

PhDClustering and cooperative transmission are the key enablers in power-constrained Internet of Things (IoT) networks. The challenges for power-constrained devices in IoT networks are to reduce the energy consumption and to guarantee the Quality of Service (QoS) provision. In this thesis, optimal node selection algorithms based on clustering and cooperative communication are proposed for different network scenarios, in particular: • The QoS-aware energy efficient cluster heads (CHs) selection algorithm in one-hop capillary networks. This algorithm selects the optimum set of CHs and construct clusters accordingly based on the location and residual energy of devices. • Cooperative nodes selection algorithms for cluster-based capillary networks. By utilising the spacial diversity of cooperative communication, these algorithms select the optimum set of cooperative nodes to assist the CHs for the long-haul transmission. In addition, with the regard of evenly energy distribution in one-hop cluster-based capillary networks, the CH selection is taken into consideration when developing cooperative devices selection algorithms. The performance of proposed selection algorithms are evaluated via comprehensive simulations. Simulation results show that the proposed algorithms can achieve up to 20% network lifetime longevity and up to 50% overall packet error rate (PER) decrement. Furthermore, the simulation results also prove that the optimal tradeoff between energy efficiency and QoS provision can be achieved in one-hop and multi-hop cluster-based scenarios.Chinese Scholarship Counci

Proceedings of the COVid-19 Empirical Research (COVER) Conference: Italy, October 30th, 2020

The Covid-19 pandemic has spread across the world at a rate never seen before, affecting different countries and having a huge impact not only on health care systems but also on economic systems. Never as in this situation the continuous exchange of views between scientists of different disciplines must be considered the keystone to overcome this emergency. The dramatic global situation has prompted many researchers from different fields to focus on studying the Covid-19 pandemic and its economic and social implications in a multi-facet fashion. This volume collects the contributions to the COVid-19 Empirical Research (COVER) Conference, organized by the Centre of Excellence in Economics and Data Science of the Department of Economics, Management and Quantitative Methods, University of Milan, Italy, October 30th, 2020. This conference aimed to collect different points of view by opening an interdisciplinary discussion on the possible developments of the pandemic. The conference contributions ranged in the social, economic and mathematical-statistical areas.illustratorThe Covid-19 pandemic has spread across the world at a rate never seen before, affecting different countries and having a huge impact not only on health care systems but also on economic systems. Never as in this situation the continuous exchange of views between scientists of different disciplines must be considered the keystone to overcome this emergency. The dramatic global situation has prompted many researchers from different fields to focus on studying the Covid-19 pandemic and its economic and social implications in a multi-facet fashion. This volume collects the contributions to the COVid-19 Empirical Research (COVER) Conference, organized by the Centre of Excellence in Economics and Data Science of the Department of Economics, Management and Quantitative Methods, University of Milan, Italy, October 30th, 2020. This conference aimed to collect different points of view by opening an interdisciplinary discussion on the possible developments of the pandemic. The conference contributions ranged in the social, economic and mathematical-statistical areas