Consistent hashing is a technique for distributing data across a network of nodes in a way that minimizes reorganization when nodes join or leave the network. It is extensively applied in modern distributed systems as a fundamental mechanism for routing and data placement. Similarly, distributed storage systems rely on consistent hashing for scalable and fault-tolerant data partitioning. This paper introduces BinomialHash, a consistent hashing algorithm that executes in constant time and requires minimal memory. We provide a detailed explanation of the algorithm, present a pseudocode implementation, and formally establish its strong theoretical guarantees. Finally, we compare its performance against state-of-the-art constant-time consistent hashing algorithms, demonstrating that our solution is both highly competitive and effective, while also validating the theoretical boundaries.

Consistent hashing is used in distributed systems and networking applications to spread data evenly and efficiently across a cluster of nodes. In this paper, we present MementoHash, a novel consistent hashing algorithm that eliminates known limitations of state-of-the-art algorithms while keeping optimal performance and minimal memory usage. We describe the algorithm in detail, provide a pseudo-code implementation, and formally establish its solid theoretical guarantees. To measure the efficacy of MementoHash, we compare its performance, in terms of memory usage and lookup time, to that of state-of-the-art algorithms, namely, AnchorHash, DxHash, and JumpHash. Unlike JumpHash, MementoHash can handle random failures. Moreover, MementoHash does not require fixing the overall capacity of the cluster (as AnchorHash and DxHash do), allowing it to scale indefinitely. The number of removed nodes affects the performance of all the considered algorithms. Therefore, we conduct experiments considering three different scenarios: stable (no removed nodes), one-shot removals (90% of the nodes removed at once), and incremental removals. We report experimental results that averaged a varying number of nodes from ten to one million. Results indicate that our algorithm shows optimal lookup performance and minimal memory usage in its best-case scenario. It behaves better than AnchorHash and DxHash in its average-case scenario and at least as well as those two algorithms in its worst-case scenario.

When a cluster is scaled, a well-known hashing technique called consistent hashing permits only a small number of resources to be remapped. In a variety of settings, including distributed databases, cloud infrastructures, and peer-to-peer networks, it performs key functions as a data router and load balancer. The literature does not offer a full review and comparative assessment of consistent hashing algorithms, despite the fact that research on these algorithms in relation to various usage scenarios has been conducted. Consequently, in a rather neutral setting, this study surveys and empirically compares the most widely used consistent hashing algorithms for distributed databases and cloud infrastructures, published from 1997 to 2021. All algorithms have been implemented in Java and benchmarked on common hardware to perform the comparison. We found Jump, Anchor, and Dx to outmatch the other algorithms on all the considered metrics. The measured values match the asymptotic curves. Although, some asymptotically faster algorithms have been shown to be slower in practice due to the number of memory accesses.

Emotions play a significant role in product design for end-users.However, how to take emotions into account is not yet completely understood.We argue that this gap is due to a lack of methodological and technological frameworks for effective investigation of the elicitation conditions related to emotions and corresponding emotional responses of the users.Emotiondriven design should encompass a thorough assessment of users' emotional reactions in relation to certain elicitation conditions.By using Virtual Reality (VR) as mean to perform this investigation, we propose a novel methodological framework, referred to as the VR-Based Emotion-Elicitation-and-Recognition loop (VEE-loop), to close this gap.

BACKGROUND: Current alignment tools typically lack an explicit model of indel evolution, leading to artificially short inferred alignments (i.e., over-alignment) due to inconsistencies between the indel history and the phylogeny relating the input sequences. RESULTS: We present a new progressive multiple sequence alignment tool ProPIP. The process of insertions and deletions is described using an explicit evolutionary model-the Poisson Indel Process or PIP. The method is based on dynamic programming and is implemented in a frequentist framework. The source code can be compiled on Linux, macOS and Microsoft Windows platforms. The algorithm is implemented in C++ as standalone program. The source code is freely available on GitHub at https://github.com/acg-team/ProPIP and is distributed under the terms of the GNU GPL v3 license. CONCLUSIONS: The use of an explicit indel evolution model allows to avoid over-alignment, to infer gaps in a phylogenetically consistent way and to make inferences about the rates of insertions and deletions. Instead of the arbitrary gap penalties, the parameters used by ProPIP are the insertion and deletion rates, which have biological interpretation and are contextualized in a probabilistic environment. As a result, indel rate settings may be optimised in order to infer phylogenetically meaningful gap patterns.

The increasing spreading of top-medium range mobile phones offering advanced capabilities and the broader use of short-range wireless technologies embedded in them are laying the foundations for the development of new added-value services. In this paper we present an architectural solution for delivering location-based services over heterogeneous mobile phones tailored to the preferences of individual users. We then discuss the performance of a solution for proximity marketing derived from the platform, in terms of localization accuracy and client-application usability.

The level of automation in factories and plants, and the need for their fast design and customization, increases steadily. These systems are often designed with various means, and controlled by heterogeneous embedded hard- and software. These circumstances raise the problem complexity and the time to be invested. The state-of-the art and the trends in design and engineering for the industrial automation have not yet provided an applicable solution to the mentioned issues. Hence a new European project tries to overcome the previous problems with a new component based approach for automatically linking the design and implementation phases. This document resumes and analyses the results of a survey conducted among the project industrial partners about their current design and implementation processes. What emerges is the need and the lack of connection, and even transformation rules, between the two aforementioned processes.

Support for evolutionary design approaches is not a very much investigated topic in the automation domain. Advanced facilities for semantic validation, refactoring and model transformations, which are necessary for rapid prototyping of control applications, are seldom found in design tools. Implementing such facilities is even more difficult when using the IEC 61499 standard, through some characteristics of the persistence model. This paper presents a specialized development environment for modelling and automatically generating applications for the industrial automation field, its unique facilities for supporting evolutionary design and the suggested improvements to the IEC 61499 model. This environment was developed inside a project for assessing the IEC 61499 principles and for verifying design approaches and execution models on a real plant.