Deferral: on the Feasibility of High-Volume Blockchain-based Referral Systems

Digital marketing has transformed referral marketing, revealing limitations in traditional centralized systems such as trust, transparency, and efficiency, however, the potential advantages of decentralized systems remain underexplored. This paper investigates the feasibility of a high-volume, decentralized referral system. The approach assesses smart contract prototypes for cost-effectiveness and performance in high-user engagement scenarios in different EVM-compatible blockchains and referral strategies, such as multilevel referrals. Findings confirm the technical viability as a blueprint for designing and implementing similar systems, highlighting challenges in real-world deployments, such as Sybil attacks, and the interplay between technical and economical design factors

Automatic network configuration with dynamic churn prediction

Peer-to-Peer (P2P) systems have been deployed on millions of nodes worldwide in environments that range from static to very dynamic and therefore exhibit different churn levels. Typically, P2P systems introduce redundancy to cope with loss of nodes. In distributed hash tables, redundancy often fixed during development or at initial deployment of the system. This can limit the applicability of the system to stable environments or make them inefficient in such environments. Automatic network configuration can make a system more adaptable to changing environments and reduce manual configuration tasks. Therefore, this paper proposes an automatic replication configuration based on churn prediction that automatically adapts its replication configuration to its environment. The mechanism termed dynamic replication mechanism (dynamic RM) developed and evaluated in this paper is based on exponential moving averages to predict churn that is used itself to determine a replication factor meeting a certain reliability threshold. Simulations with synthetic data and experiments with data from torrent trackers show that the behavior can be predicted accurately in any environment, from low churn rates to diurnal and high churn rates

Developmental patterns of glycolytic enzymes in regenerating skeletal muscle after autogenous free grafting

Extensor digitorum longus muscles of rats were removed and injected with a solution of Marcaine plus hyaluronidase. After incubation in Marcaine solution for 10 min, the muscles were grafted into their original beds. The grafts and the contralateral control muscles were removed from the rats at 0, 1-5, 7, 11, 36, and 69 days postoperatively. The muscles were then frozen in dry ice and isopentane and subsequently homogenized and centrifuged. The supernatant was analyzed for a number of enzymes, the regenerative patterns of which can be classified into 3 groups: (1) early increase in activity: hexokinase, glucose-6-phosphate dehydrogenase; (2) early decrease in activity with failure to recover to control levels: phosphorylase, phosphofructokinase, [alpha]-glycerophosphate dehydrogenase; and (3) early decrease followed by return to control levels: lactate dehydrogenase, pyruvate kinase, creatine phosphokinase, adenylate kinase. These patterns are not identical to those reported for embryogenesis of muscle. The data are discussed with regard to correlative histological studies of muscle regeneration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/22796/1/0000352.pd

Diversity in warning coloration: selective paradox or the norm?

Aposematic theory has historically predicted that predators should select for warning signals to converge on a single form, as a result of frequency-dependent learning. However, widespread variation in warning signals is observed across closely related species, populations and, most problematically for evolutionary biologists, among individuals in the same population. Recent research has yielded an increased awareness of this diversity, challenging the paradigm of signal monomorphy in aposematic animals. Here we provide a comprehensive synthesis of these disparate lines of investigation, identifying within them three broad classes of explanation for variation in aposematic warning signals: genetic mechanisms, differences among predators and predator behaviour, and alternative selection pressures upon the signal. The mechanisms producing warning coloration are also important. Detailed studies of the genetic basis of warning signals in some species, most notably Heliconius butterflies, are beginning to shed light on the genetic architecture facilitating or limiting key processes such as the evolution and maintenance of polymorphisms, hybridisation, and speciation. Work on predator behaviour is changing our perception of the predator community as a single homogenous selective agent, emphasising the dynamic nature of predator-prey interactions. Predator variability in a range of factors (e.g. perceptual abilities, tolerance to chemical defences, and individual motivation), suggests that the role of predators is more complicated than previously appreciated. With complex selection regimes at work, polytypisms and polymorphisms may even occur in Mullerian mimicry systems. Meanwhile, phenotypes are often multifunctional, and thus subject to additional biotic and abiotic selection pressures. Some of these selective pressures, primarily sexual selection and thermoregulation, have received considerable attention, while others, such as disease risk and parental effects, offer promising avenues to explore. As well as reviewing the existing evidence from both empirical studies and theoretical modelling, we highlight hypotheses that could benefit from further investigation in aposematic species. Finally by collating known instances of variation in warning signals, we provide a valuable resource for understanding the taxonomic spread of diversity in aposematic signalling and with which to direct future research. A greater appreciation of the extent of variation in aposematic species, and of the selective pressures and constraints which contribute to this once-paradoxical phenomenon, yields a new perspective for the field of aposematic signalling.Peer reviewe

Demonstration of the CompactPSH Incentive Scheme in a Peer-to-Peer Streaming Application

This demo shows the CompactPSH incentive scheme integrated in LiveShift. LiveShift is a peer-to-peer video streaming application, which supports both live streaming and video-on-demand. Peers participate in a distributed storage, which adds the ability to replay time-shifted streams from other peers in a distributed manner. The incentive scheme is in place to encourage peers to provide stored video data. CompactPSH allows peers to establish direct and indirect reciprocity to determine the contribution of peers. Thus, non-cooperative peers get a lower video quality than cooperative peers

B-Tracker: Improving load balancing and efficiency in distributed P2P trackers

Trackers are used in peer-to-peer (P2P) networks for provider discovery, that is, mapping resources to potential providers. Centralized trackers, e.g., as in the original BitTorrent protocol, do not benefit from P2P properties, such as no single point of failure, scalability, and load balancing. Decentralized mechanisms have thus been proposed, based on distributed hash tables (DHTs) and gossiping, such as BitTorrent's Peer Exchange (PEX). While DHT-based trackers suffer from load balancing problems, gossip-based ones cannot deliver new mappings quickly. This paper presents B-Tracker, a fully-distributed, pull-based tracker. B-Tracker extends DHT functionality by distributing the tracker load among all providers in a swarm. Bloom filters are used to avoid redundant mappings to be transmitted. This results in the important properties of load balancing and scalability, while adding the ability for peers to fetch new mappings instantly. B-Tracker shows, through simulations, improved load balancing and better efficiency when compared to pure DHTs and PEX

Abstract

Fast similarity search is important for time-sensitive applications. Those include both enterprise and web scenarios, where typos, misspellings, and noise need to be removed in an efficient way, in order to improve data quality, or to find all information of interest to the user. This paper presents a new algorithm called Fast Similarity Search (FastSS) that performs an exhaustive similarity search in a dictionary, based on the edit distance model of string similarity. The algorithm uses deletions to model the edit distance. For a dictionary containing n words of average length m, and given a maximum number of spelling errors k, FastSS uses a deletion dictionary of size O(nm k). At search time each query is mutated to generate a deletion neighborhood of size O(m k), which is compared to the indexed deletion dictionary. As a deletion neighborhood is smaller than a neighborhood using deletions, insertions and replacements, this contributes to a faster search. FastSS looks up misspellings in a time which is independent of n fo