Reviewerly/exHarmony huggingface dataset:
Evolutionary Computation and Evolutionary Deep Learning for Image Analysis, Signal Processing and Pattern Recognition

tutorial Share on Evolutionary Computation and Evolutionary Deep Learning for Image Analysis, Signal Processing and Pattern Recognition Authors: Mengjie Zhang Victoria University of Wellington, Wellington, New Zealand Victoria University of Wellington, Wellington, New Zealand https://orcid.org/0000-0003-4463-9538View Profile , Stefano Cagnoni University of Parma, Parma, Italy University of Parma, Parma, Italy https://orcid.org/0000-0003-4669-512XView Profile Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1533–1561https://doi.org/10.1145/3583133.3595045Published:24 July 2023Publication History 0citation22DownloadsMetricsTotal Citations0Total Downloads22Last 12 Months22Last 6 weeks16 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Incremental Recursive Ranking Grouping -- A Decomposition Strategy for Additively and Nonadditively Separable Problems

Many real-world optimization problems may be classified as Large-Scale Global Optimization (LSGO) problems. When these high-dimensional problems are continuous, it was shown effective to embed a decomposition strategy into a Cooperative Co-Evolution (CC) framework. The effectiveness of the method that decomposes a problem into subproblems and optimizes them separately may depend on the decomposition accuracy and cost. Recent decomposition strategy advances focus mainly on Differential Grouping (DG). However, when a considered problem is nonadditively separable, DG-based strategies may report some variables as interacting, although the interaction between them does not exist. Monotonicity checking strategies do not suffer from this disadvantage. However, they suffer from another decomposition inaccuracy - monotonicity checking strategies may miss discovering many existing interactions. Therefore, Incremental Recursive Ranking Grouping (IRRG) is a new proposition that accurately decomposes both additively and nonadditively separable problems. The decomposition cost of IRRG is higher when compared with Recursive DG 3 (RDG3). Since the higher cost was a negligible part of the overall computational budget, optimization results of the considered CC frameworks were affected mainly by the decomposition accuracy.
Lifting String Analysis Domains

Strings are characterized by properties that either refer to their content (e.g., the set of contained characters) and to their shape (e.g., the character position and the length of the strings). In this paper, we present a general framework providing a systematic lifting of string domains through a segmentation abstraction, yielding to a more accurate representation of strings without major impact on efficiency of the analysis. The proposed operator exploits the abstraction functor introduced by Cousot, Cousot and Logozzo for a fully parametric array representation, in the string scenario.
Genetic Improvement: Taking real-world source code and improving it using computational search methods

tutorial Open Access Share on Genetic Improvement: Taking real-world source code and improving it using computational search methods Authors: Alexander E. I. Browniee Computing and Mathematics, University of Stirling, Stirling, Scotland Computing and Mathematics, University of Stirling, Stirling, Scotland https://orcid.org/0000-0003-2892-5059View Profile , Saemundur O. Haraldsson Computing and Mathematics, University of Stirling, Stirling, Scotland Computing and Mathematics, University of Stirling, Stirling, Scotland https://orcid.org/0000-0003-0395-5884View Profile , John R. Woodward Loughborough University, Loughborough, UK Loughborough University, Loughborough, UK https://orcid.org/0000-0002-2093-8990View Profile Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1213–1247https://doi.org/10.1145/3583133.3595044Published:24 July 2023Publication History 0citation20DownloadsMetricsTotal Citations0Total Downloads20Last 12 Months20Last 6 weeks17 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
A Fully Synthesizable 100Mbps Edge-Chasing True Random Number Generator

This paper presents a fully synthesizable True Random Number Generator (TRNG) based on edge chasing in even-stage ring oscillators (RO). The proposed configurable RO with an automatic self-calibration loop enables robust high-speed operations and high-quality outputs under -40 to 125°C and 0.7 to 1.2V conditions, digital layout mismatch and process variations, and power injection attacks. Ten 65nm prototypes designed in unmodified digital flow using only foundry standard cells demonstrate random bit generation at up to 100.8Mbps and pass all NIST tests without post-processing.
Algorithm Configuration via Continuously Racing: Preliminary Results

Automatic algorithm configuration procedures aim at supporting the design and application of optimization algorithms by providing specialized tools to automatically adjust their parameters to use the available computational resources effectively. The irace configurator is a state-of-the-art method implementing an iterated racing procedure that, in its current implementation, allows parallel executions of the target algorithm. Parallel evaluation is crucial for the efficient use of computational resources, reducing the wall-clock time that the configuration process needs to find a good configuration. In this work, we propose an alternative to irace that performs a single race instead of iterative races. The continuously racing configurator (crace) evaluates, removes and generates new configurations asynchronously, granting a high level of flexibility regarding the configuration process when compared to the previous iterative scheme. In this paper, we provide a general description of the crace configuration procedure and perform initial exploratory experiments on five configuration scenarios. These experiments focus on evaluating the effect of the minimum number of configurations required to be involved in the race and the number of parallel evaluations. The first results are encouraging, showing that crace can be a competitive procedure on the scenarios evaluated.
Genetic Algorithm-Based Path Planning for Autonomous Mobile Robots

In this paper, a Genetic Algorithm is used to solve the path planning problem for autonomous mobile robots in static environments. The goal of the path planning problem is to find a valid and practical path between two points while avoiding obstacles and optimizing a number of criteria including path length, safety, and distance from obstacles. A quality function is proposed to evaluate the optimization approach for different scenarios. Experimental results show that enhanced solutions can be achieved in less time using optimal values of the search algorithm parameters.
Comparing Individual Representations in Grammar-Guided Genetic Programming for Glucose Prediction in People with Diabetes

The representation of individuals in Genetic Programming (GP) has a large impact on the evolutionary process. In this work, we investigate the evolutionary process of three Grammar-Guided GP (GGGP) methods, Context-Free Grammars GP (CFG-GP), Grammatical Evolution (GE) and Structured Grammatical Evolution (SGE), in the context of the complex, real-world problem of predicting the glucose level of people with diabetes two hours ahead of time. Our analysis differs from previous analyses by (1) comparing all three methods on a complex benchmark, (2) implementing the methods in the same framework, allowing a fairer comparison, and (3) analyzing the evolutionary process outside of performance. We conclude that representation choice is more impactful with a higher maximum depth, and that CFG-GP better explores the search space for deeper trees, achieving better results. Furthermore, we find that CFG-GP relies more on feature construction, whereas GE and SGE rely more on feature selection. Finally, we altered the GGGP methods in two ways: using ε-lexicase selection, which solved the overfitting problem of CFG-GP; and with a penalization of complex trees, to create more interpretable trees. Combining ε-lexicase selection with CFG-GP performed best.
Giving in English and Norwegian

This chapter investigates ditransitive constructions and their prepositional dative alternates, containing the cognate verbs English give and Norwegian gi , using data from the English–Norwegian Parallel Corpus (Johansson 2007). The bi-directional nature of the corpus enables us not only to compare the distribution of constructions in the original texts in the two languages but also to investigate how individual tokens are translated into the other language. The analysis shows that give constructions in the two languages are remarkably similar in their semantics and their distribution. There are, however, some differences, including the greater incidence of light verb constructions in English, and a tendency for Norwegian translators to employ a get construction with the recipient recoded as subject rather than indirect/prepositional object.
Unexplained Fluctuations in Particle Swarm Optimisation Performance with Increasing Problem Dimensionality

We study the behaviour of particle swarm optimisation (PSO) with increasing problem dimension for the Alpine 1 function as an exploratory and preliminary case study. Performance trends are analysed and the tuned population size for PSO across dimensions is considered. While performance generally decreases monotonically with scale, there is an unexpected improvement in performance part way along the trend. This also appears to coincide with a counterintuitive transition from large to small populations being preferred, and underlines the challenge, and importance of, selecting the right algorithm and configuration for the problem at each increase in dimensionality.
Object Affinity Learning: Towards Annotation-Free Instance Segmentation

We address the problem of annotation-free instance segmentation in the wild, aiming to relieve the expensive cost of manual mask annotations. Existing approaches utilize appearance cues, such as color, edge, and texture information, to generate pseudo masks for instance segmentation. However, due to the ambiguity of defining an object by visual appearance alone, these methods fail to distinguish objects from the background under complex scenes. Beyond visual cues, objects are one-piece in space and move together over time, which indicates that geometry cues, such as spatial continuity and motion consistency, are also exploitable for this problem. To directly utilize geometry cues, we propose an affinity-based paradigm for annotation-free instance segmentation. The new paradigm is called object affinity learning, a proxy task of annotation-free instance segmentation, which aims to tell whether two pixels come from the same object by learning feature representation from geometry cues. During inference, the learned object affinity could be further converted into instance segmentation masks by some graph partition algorithms. The proposed object affinity learning achieves much better instance segmentation performance than existing pseudo-mask-based methods on the large-scale Waymo Open Dataset and KITTI dataset.
An Improved Hybrid Multi-Objective Particle Swarm Optimization to Enhance Convergence and Diversity

Particle Swarm Optimization (PSO) has received increasing attention from researchers due to its fast convergence ability. This paper proposes a new improvement in Multi-objective Particle Swarm Optimization based on Pareto dominance. This research aims to develop an efficient exploration and exploitation algorithm with faster convergence using a limited iterative process. Researchers have already proved that popular EA algorithms can converge faster as well as provide efficient exploration. However, there are some shortcomings in establishing success in smaller iterative outcomes after maintaining both population diversity as well as best optimal solutions. Therefore, our proposed approach incorporates the hybrid evolutionary computation process with PSO to address multi-objective problems. The results indicate that the proposed method is highly competitive and is able to approximate the Pareto Front where other algorithms struggle.
Cutting Onions With Others' Hands: A First Measurement of Tor Proxies in the Wild

Tor Proxies are principally designed for allowing users to easily access hidden services in Tor network through standard browsers instead of dedicated software (e.g., Tor Browser), which is user-friendly but at the expense of anonymity. At present, Tor proxies are volunteer-run without any central management. As a result, it still remains unclear the scale and usage of Tor proxies in the wild throughout the Internet. In light of this, we perform the first large-scale measurement study of Tor proxies through passively identifying Tor proxies worldwide and then conduct initial analysis based on the identified Tor proxies. We cooperate with one of the most popular public DNS resolvers and collect passive DNS for the last 4 years with hundreds of billions of raw DNS records per day. To analyze the usage of Tor proxies, we also crawl newly observed webpages of hidden services accessed via Tor proxies for two years. We propose several techniques to identify the Tor proxies and find about 700 historically valid and 130 online Tor proxies in over 30 countries. We also provide several insightful findings and promising directions to motivate future work on this topic.
Threat Evaluation of Enterprise Utilizations via Graphical Modeling

Studying cyber resilience has proved to be a difficult technological obstacle. Numerous case studies have been performed to assess the cyber resilience of enterprise business systems using attack graphs. removing the assets level, service resource, activity surface for the commercial processes, etc.. from a generic business enterprise system is challenging for automation since here is where task dependencies and formal vulnerability description meet to build attack graphs. We provide a model for threat analysis of a company based on a specified group of vulnerabilities spread across many layers of business operations. First, we collect the company's task dependencies from its process flow (BPMN), and afterwards we retrieve the hierarchy dependencies that includes the asset-, service-, and process management components. We infer a logical concept of security risk transmission from the graphical reliance structure and the susceptibility criteria in order to build MulVAL multi-step, multi-stage attacks. The MulVAL-created attack graph is imported into the graph database Neo4J to provide online/real-time dynamic security risk dissemination analysis. In addition, we detail how the system's risk analysis may be achieved with the inclusion of certain parameters. Since we took an all-encompassing approach, threat analysis is now flexible and extensible. We show how our approach can be applied to larger systems and how graphical modeling can be used to investigate risk evaluations for enterprise software. In turn, this enables the use of a wide range of mitigation techniques for reducing the spread of threats and vulnerabilities.
Digital object identifier: What it is and why it matters?

Digital object identifiers (DOIs) have become essential tools in academic publishing for linking and identifying digital objects. This review examines the structure and assignment process of DOIs, shedding light on their critical importance in scholarly communication. DOIs consist of a unique alphanumeric string, including a prefix and a suffix, which serves as a persistent link to digital objects. By providing a standardized identifier, DOIs enhance the discoverability, accessibility, and referencing of various digital objects, including journal articles. The DOI also plays a crucial role in article promotion. With a DOI, authors, publishers, and researchers can effectively share and promote their articles, facilitating their discoverability in academic and research communities. However, challenges may arise with DOIs, such as non-functional links. This review provides troubleshooting tips for resolving such issues, including checking for typographical errors, validating the source, and seeking assistance from the DOI registration agency or the publisher.
Evaluating the Effectiveness of Blockchain in Supply Chain Finance

The Covid-19 pandemic has highlighted the inadequacies of traditional paper-based supply chain finance and trade systems. Digitalization offers a solution to reduce friction in global trade. This evaluation examines the latest commercial applications and theoretical perspectives on the use of blockchain technology in supply chain finance systems as a medium for digitalization. We argue that blockchain technology has the potential to revolutionize distribution network finance by eliminating inefficiencies and improving communication between parties, which are currently major obstacles to progress in this field. We examine the financial, operational, and legal challenges in supply chain finance and evaluate how blockchain technology can address these challenges through a review of the literature and analysis of industrial approaches. We discuss both the limitations and benefits of blockchain technology and identify the necessary conditions for the implementation of blockchain-enabled supply chains.
Graph Convolutional Reinforcement Learning for Load Balancing and Smart Queuing

In this paper, we propose a graph convolutional deep reinforcement learning framework for both smart load balancing and queuing agents in a collaborative environment. We aim to balance traffic loads on different paths, and then control how packets belonging to different flow classes are dequeued at network nodes. Our objective is twofold: first to improve general network performance in terms of throughput and end-to-end delay, and second, to ensure meeting stringent service level agreements for a set of classified network flows. Our proposals use attention mechanisms to extract relevant features from local observations and neighborhood policies to limit the overhead of inter-agent communications. We assess our algorithms in a Mininet testbed and show that they outperform classic approaches to load balancing and smart queuing in terms of throughput and end-to-end delay.
Optimization of Load Balancing Algorithm in Cloud Computing

A rapidly expanding service, cloud computing (CC) uses a pay-per-use business model. As far as capacity, organization, web administrations, and so forth, innovation offers various administrations. Regardless, the expansion of these organizations and the enormous flood in user demand has made it trying to stay aware of execution as per QoS assessment and SLA chronicles that cloud suppliers make available to businesses. This growth brought about difficulties like load balancing. In addition, it became challenging to meet customer expectations for response speed and work scheduling deadlines. This research suggests an optimal approach based on schedule limitations using the Machine Learning Classification technique to overcome these issues. The main goals of the suggested technique are to increase productivity, optimise server resources by taking into account the importance of various users' tasks, and prevent server failure. Based on the most recent literature, our suggested method will address the aforementioned problems and the existing research gap.
COVID-19 Contact Tracing Mobile Apps: A Comparative Perspective using the “Cascade of Deference”

This article frames the action imperative as an ethical dilemma for governance actors. It situates the debate in the context of the design and implementation of digital contact tracing applications to mitigate the spread of the COVID-19 pandemic. Using a normative case study approach, it seeks to answer the question—how can the ethical dilemma of an action imperative in the face of scientific and normative uncertainty, be resolved by governance actors in ways that can minimize harms? Drawing insights from the field of Science and Technology Studies, this article makes the case that when scientific evidence claims are weak, the action imperative should follow a cascade of deference with objectivity, consensus, precaution, and subsidiarity as stopping points. It concludes by making a broader case to promote moral reasoning concerning the substantive goals of digitalization.
A Usable Enhanced Dynamic BFT Protocol

Block chain technology, which evolved as a distributed system preserving a public ledger through consensus protocol, is showing great promise in many different contexts, including the supply chain, the financial sector, the internet of things (IoT), and so on. Important in the development of the block chain infrastructure is the use of consensus mechanisms, and one such mechanism is Byzantine Fault Tolerance (BFT) protocols. However, the vast majority of BFT protocols are inherently unable to be used in realistic settings due to their lack of dynamic features (i.e., nodes can't join or leave a functioning system) and the inability to penalize unsuccessful nodes. In this paper, we provide the Dynamic Enhanced BFT (DEBFT) protocol for handling dynamic properties and faulty node penalties. DEBFT, which is based on Honey Badger BFT, uses Dynamic Threshold Identity-based Encryption and Distributed Key Generation to facilitate consensus group changes without requiring a complete system reconfiguration. Additionally, assessment metrics are provided to evaluate consensus nodes and remove problematic nodes.
Fast, Flexible, and Fearless: A Rust Framework for the Modular Construction of Metaheuristics

We present MAHF, a Rust framework for the modular construction and subsequent evaluation of evolutionary algorithms, but also any other metaheuristic framework, including non-population-based and constructive approaches. We achieve high modularity and flexibility by splitting algorithms into components with a uniform interface and allowing communication through a shared blackboard. Nevertheless, MAHF is aimed at being easy to use and adapt to the specific purposes of different practitioners. To this end, this paper focuses on providing a general description of the design of MAHF before illustrating its application with a variety of different use cases, ranging from simple extension of the set of implemented components and the subsequent construction of algorithms not present within the framework to hybridization approaches, which are often difficult to realize in specialized software frameworks. By providing these comprehensive examples, we aim to encourage others to utilize MAHF for their needs, evaluate its effectiveness, and improve upon its application.