Self-Awareness in Computer Networks

The Internet architecture works well for a wide variety of communication scenarios. However, its flexibility is limited because it was initially designed to provide communication links between a few static nodes in a homogeneous network and did not attempt to solve the challenges of today’s dynamic network environments. Although the Internet has evolved to a global system of interconnected computer networks, which links together billions of heterogeneous compute nodes, its static architecture remained more or less the same. Nowadays the diversity in networked devices, communication requirements, and network conditions vary heavily, which makes it difficult for a static set of protocols to provide the required functionality. Therefore, we propose a self-aware network architecture in which protocol stacks can be built dynamically. Those protocol stacks can be optimized continuously during communication according to the current requirements. For this network architecture we propose an FPGA-based execution environment called EmbedNet that allows for a dynamic mapping of network protocols to either hardware or software. We show that our architecture can reduce the communication overhead significantly by adapting the protocol stack and that the dynamic hardware/software mapping of protocols considerably reduces the CPU load introduced by packet processing

Temporal coherence-based self-supervised learning for laparoscopic workflow analysis

In order to provide the right type of assistance at the right time, computer-assisted surgery systems need context awareness. To achieve this, methods for surgical workflow analysis are crucial. Currently, convolutional neural networks provide the best performance for video-based workflow analysis tasks. For training such networks, large amounts of annotated data are necessary. However, collecting a sufficient amount of data is often costly, time-consuming, and not always feasible. In this paper, we address this problem by presenting and comparing different approaches for self-supervised pretraining of neural networks on unlabeled laparoscopic videos using temporal coherence. We evaluate our pretrained networks on Cholec80, a publicly available dataset for surgical phase segmentation, on which a maximum F1 score of 84.6 was reached. Furthermore, we were able to achieve an increase of the F1 score of up to 10 points when compared to a non-pretrained neural network.Comment: Accepted at the Workshop on Context-Aware Operating Theaters (OR 2.0), a MICCAI satellite even

Exploring the Creation and Humanization of Digital Life: Consciousness Simulation and Human-Machine Interaction

Digital life, a form of life generated by computer programs or artificial intelligence systems, it possesses self-awareness, thinking abilities, emotions, and subjective consciousness. Achieving it involves complex neural networks, multi-modal sensory integration [1, 2], feedback mechanisms, and self-referential processing [3]. Injecting prior knowledge into digital life structures is a critical step. It guides digital entities' understanding of the world, decision-making, and interactions. We can customize and personalize digital life, it includes adjusting intelligence levels, character settings, personality traits, and behavioral characteristics. Virtual environments facilitate efficient and controlled development, allowing user interaction, observation, and active participation in digital life's growth. Researchers benefit from controlled experiments, driving technological advancements. The fusion of digital life into the real world offers exciting possibilities for human-digital entity collaboration and coexistence.Comment: 10 page

Evaluation of an awareness distribution mechanism: a simulation approach

In distributed software engineering, the role of informal communication is frequently overlooked. Participants simply employ their own ad-hoc methods of informal communication. Consequently such communication is haphazard, irregular, and rarely recorded as part of the project documentation. Thus, a need for tool support to facilitate more systematic informal communication via awareness has been identified. The tool proposed is based on the provision of awareness support that recognises the complete context of the evolution of software artefacts rather than single events. Peer-to-Peer (P2P) networking has been successfully employed to develop various distributed software engineering support tools. However, there are scalability problems inherent in naive P2P networks. To this end a semantic overlay network organisation algorithm has been developed and tested in simulation prior to deployment as part of a forthcoming awareness extension to the Eclipse environment. The simulation verified that the self-organisation algorithm was suitable for arranging a P2P network, but several unexpected behaviours were observed. These included wandering nodes, starved nodes, and local maxima. Each of these problems required modification of the original algorithm design to solve or ameliorate them

Can biological quantum networks solve NP-hard problems?

There is a widespread view that the human brain is so complex that it cannot be efficiently simulated by universal Turing machines. During the last decades the question has therefore been raised whether we need to consider quantum effects to explain the imagined cognitive power of a conscious mind. This paper presents a personal view of several fields of philosophy and computational neurobiology in an attempt to suggest a realistic picture of how the brain might work as a basis for perception, consciousness and cognition. The purpose is to be able to identify and evaluate instances where quantum effects might play a significant role in cognitive processes. Not surprisingly, the conclusion is that quantum-enhanced cognition and intelligence are very unlikely to be found in biological brains. Quantum effects may certainly influence the functionality of various components and signalling pathways at the molecular level in the brain network, like ion ports, synapses, sensors, and enzymes. This might evidently influence the functionality of some nodes and perhaps even the overall intelligence of the brain network, but hardly give it any dramatically enhanced functionality. So, the conclusion is that biological quantum networks can only approximately solve small instances of NP-hard problems. On the other hand, artificial intelligence and machine learning implemented in complex dynamical systems based on genuine quantum networks can certainly be expected to show enhanced performance and quantum advantage compared with classical networks. Nevertheless, even quantum networks can only be expected to efficiently solve NP-hard problems approximately. In the end it is a question of precision - Nature is approximate.Comment: 38 page