Miracle-dz/UniFAR-FTUs huggingface dataset:
The Bayesian learning classifier system: implementation, replicability, comparison with XCSF

Learning Classifier Systems (LCSs) are a family of versatile rule-based machine learning algorithms. Despite their long research history, the foundations of most LCSs are still informal due to them having been developed in an ad-hoc manner. An exception to this is the fully Bayesian LCS described by Drugowitsch in his 2008 book. In the present paper we shortly reiterate the central points of that system and then showcase our Python implementation of it, reporting on our attempt at replicating Drugowitsch's empirical results as well as on the results of a preliminary comparison study with the well-known LCS XCSF on the same learning tasks. We are able to replicate parts of Drugowitsch's results and explain the remaining differences. The comparison results make us conclude that the system may be competitive and exhibits unique features. Finally, we identify its current greatest shortcomings and based on those the next steps towards making it more universally usable.
Optimising autonomous robot swarm parameters for stable formation design

Autonomous robot swarm systems allow to address many inherent limitations of single robot systems, such as scalability and reliability. As a consequence, these have found their way into numerous applications including in the space and aerospace domains like swarm-based asteroid observation or counter-drone systems. However, achieving stable formations around a point of interest using different number of robots and diverse initial conditions can be challenging. In this article we propose a novel method for autonomous robots swarms self-organisation solely relying on their relative position (angle and distance). This work focuses on an evolutionary optimisation approach to calculate the parameters of the swarm, e.g. inter-robot distance, to achieve a reliable formation under different initial conditions. Experiments are conducted using realistic simulations and considering four case studies. The results observed after testing the optimal configurations on 72 unseen scenarios per case study showed the high robustness of our proposal since the desired formation was always achieved. The ability of self-organise around a point of interest maintaining a predefined fixed distance was also validated using real robots.
Adaptive phototaxis of a swarm of mobile robots using positive and negative feedback self-alignment

In this paper, we explore how robots in a swarm can individually exploit collisions to produce self-organizing behaviours at the macroscopic scale. We propose to focus on two behaviours that modify the orientation of a robot during a collision, which are inspired by positive and negative feedback observed in Nature. These two behaviours differ in the nature of the feedback produced after a collision by favouring either (1) the alignment or (2) the anti-alignment of the robot with an external force, whether it is an obstacle or another robot. We describe a social learning algorithm using evolutionary operators to learn individual policies that exploit these behaviours in an online and distributed fashion. This algorithm is validated both in simulation and with real robots to solve two tasks involving phototaxis, one of which requires self-organized aggregation to be completed.
TAGA: a transfer-based black-box adversarial attack with genetic algorithms

Deep learning has been widely adopted in many real-world applications, especially in image classification. However, researches have shown that minor distortions imperceptible to humans may mislead classifiers. One way to improve the robustness is using adversarial attacks to obtain adversarial examples and re-training the classifier with those images. However, the connections between attacks and application scenarios are rarely discussed. This paper proposes a novel black-box adversarial attack that is specifically designed for real-world application scenarios: The transfer-based black-box adversarial attack with genetic algorithms (TAGA). TAGA adopts a genetic algorithm to generate the adversarial examples and reduces the ensuing query costs with a surrogate model based on the transferability of adversarial attacks. Empirical results show that perturbing embeddings in the latent space helps the attack algorithm quickly obtain adversarial examples and that the surrogate fitness function reduces the number of function evaluations. Compared with several state-of-the-art attacks, TAGA improves the classifiers more under the application scenario in terms of the summation of natural and defense accuracy.
Remote acquisition and processing of cardiac signals using Arduino and Matlab

The processing of cardiac signals becomes more important and necessary every day, given the incidence of diseases that affect this vital organ worldwide. In this sense, this research work is summarized in the remote processing of cardiac signals, under the principles of telemedicine. This seeks to support the health sector in the medical diagnosis of heart disease in patients specifically in rural areas of Peru, where it is very difficult to count on the professional services of specialists. In this work, the technologies associated with the IoT are applied, in whose Internet network the processes of transmission, reception and processing of the cardiac signal are facilitated. It is important to point out that the processing of the signal is fundamental, due to the influence of external disturbances that can affect the cardiac signal, which must be presented without noise, in the process of remote diagnosis, and to achieve this purpose the Matlab platform, through console programming and there are many other platforms to achieve this end. We hope that this contribution will serve to carry out future complementary research that seeks to reduce the incidence of heart disease.
DiBB: distributing black-box optimization

DiBB (for Distributing Black-Box) is a meta-algorithm and framework that addresses the decades-old scalability issue of Black-Box Optimization (BBO), including Evolutionary Computation. Algorithmically, it does so by creating out-of-the-box a Partially Separable (PS) version of any existing black-box algorithm. This is done by leveraging expert knowledge about the task at hand to define blocks of parameters expected to have significant correlation, such as weights entering a same neuron/layer in a neuroevolution application. DiBB distributes the computation to a set of machines without further customization, while still retaining the advanced features of the underlying BBO algorithm, such as scale invariance and step-size adaptation, which are typically lost in recent distributed ES implementations. This is achieved by instantiating a separate instance of the underlying base algorithm for each block, running on a dedicated machine, with DiBB handling communication and constructing complete individuals for evaluation on the original task. DiBB's performance scales constantly with the number of parameter-blocks defined, which should allow for unprecedented applications on large clusters. Our reference implementation (Python, on GitHub and PyPI) demonstrates a 5x speed-up on COCO/BBOB using our new PS-CMA-ES. We also showcase a neuroevolution application (11 590 weights) on the PyBullet Walker2D with our new PS-LM-MA-ES.
Efficient heuristics and metaheuristics for the unrelated parallel machine scheduling problem with release dates and setup times

Parallel machine scheduling problems are among the most studied scheduling problems in the literature. We study the unrelated parallel machine scheduling problem with release dates and machine-and sequence-dependent setup times to minimize the makespan. We introduce three heuristics, five local search methods and three metaheuristics for the problem: the Late Acceptance Hill Climbing and two variants of Simulated Annealing. Furthermore, we introduce a three-set 1620-instance benchmark in which the number of jobs and machines, release dates, processing and setup times are generated according to existing procedures. The proposed approaches are analyzed and compared on the proposed benchmark. We compare the proposed heuristics and derive the best heuristic to be used to generate the initial solution of the metaheuristics. Moreover, we show that the different metaheuristics are efficient, each performing best on one of the sets.
Analyzing multi-agent reinforcement learning and coevolution in cybersecurity

Cybersecurity simulations can offer deep insights into the behavior of agents in the battle to secure computer systems. We build on existing work modeling the competition between an attacker and defender on a network architecture in a zero-sum game using a graph database linking cybersecurity attack patterns, vulnerabilities, and software. We apply coevolution to this challenging environment, and in a novel modeling approach for this problem, interpret each population as a distribution over fixed strategies to form a mixed strategy Nash equilibrium. We compare the results to solutions generated by multi-agent reinforcement learning and show that evolutionary methods demonstrate a considerable degree of robustness to parameter misspecification in this environment.
Learning the characteristics of engineering optimization problems with applications in automotive crash

Oftentimes the characteristics of real-world engineering optimization problems are not well understood. In this paper, we introduce an approach for characterizing highly nonlinear and Finite Element (FE) simulation-based engineering optimization problems, focusing on ten representative problem instances from automotive crashworthiness optimization. By computing characteristic Exploratory Landscape Analysis (ELA) features, we show that these ten crashworthiness problem instances exhibit landscape features different from classical optimization benchmark test suites, such as the widely-used Black-Box Optimization Benchmarking (BBOB) problem set. Using clustering approaches, we demonstrate that these ten problem instances are clearly distinct from the BBOB test functions. Further analysis of the crashworthiness problem instances reveal that, as far as ELA concerns, they are most similar to a class of artificially generated functions. We identify such artificially generated functions and propose to use them as scalable and fast-to-evaluate representatives of the real-world problems. Such artificially generated functions could be used for the automated design of an optimization algorithm for specific real-world problem classes.
Expensive optimization with production-graph resource constraints: a first look at a new problem class

We consider a new class of expensive, resource-constrained optimization problems (here arising from molecular discovery) where costs are associated with the experiments (or evaluations) to be carried out during the optimization process. In the molecular discovery problem, candidate compounds to be optimized must be synthesized in an iterative process that starts from a set of purchasable items and builds up to larger molecules. To produce target molecules, their required resources are either used from already-synthesized items in storage or produced themselves on-demand at an additional cost. Any remaining resources from the production process are stored for reuse for the next evaluations. We model these resource dependencies with a directed acyclic production graph describing the development process from granular purchasable items to evaluable target compounds. Moreover, we develop several resource-eficient algorithms to address this problem. In particular, we develop resource-aware variants of Random Search heuristics and of Bayesian Optimization and analyze their performance in terms of anytime behavior. The experimental results were obtained from a real-world molecular optimization problem. Our results suggest that algorithms that encourage exploitation by reusing existing resources achieve satisfactory results while using fewer resources overall.
Effects of imputation strategy on genetic algorithms and neural networks on a binary classification problem

In this paper, we compare the performance of a canonical genetic algorithm (CGA), the Self Adaptive Genetic Algorithm (SAGA), and a feed-forward neural network (FFNN) on a predictive modeling problem with incomplete data. Predictive modeling involves learning relationships between the features and labels of the data points in a dataset. Datasets with missing input values may cause problems for some learning algorithms by biasing the learned models. Imputation refers to techniques for replacing missing data through methods such as statistical probabilities, multivariate analysis, machine learning, or K-nearest neighbors. We study how imputed datasets impact the ability for CGA, SAGA, and FFNN to learn effective models. Results indicate that imputation method has little effect on CGA and SAGA performance and a noticeable effect on FFNN performance. All three algorithms perform similarly when applied to data imputed by univariate strategies, but FFNN is noticeably worse on data imputed by trained multivariate strategies. With increased quantities of imputed data, test accuracy decreases for all three algorithms while control accuracy remains surprisingly stable in all cases except for FFNN on trained multivariate imputation. Interestingly, CGA and SAGA identify the most relevant input values, even when a large amount of the data is imputed.
PROWESS: An Open Testbed for Programmable Wireless Edge Systems

Edge computing is a growing paradigm where compute resources are provisioned between data sources and the cloud to decrease compute latency from data transfer, lower costs, comply with security policies, and more. Edge systems are as varied as their applications, serving internet services, IoT, and emerging technologies. Due to the tight constraints experienced by many edge systems, research computing testbeds have become valuable tools for edge research and application benchmarking. Current testbed infrastructure, however, fails to properly emulate many important edge contexts leading to inaccurate benchmarking. Institutions with broad interests in edge computing can build testbeds, but prior work suggests that edge testbeds are often application or sensor specific. A general edge testbed should include access to many of the sensors, software, and accelerators on which edge systems rely, while slicing those resources to fit user-defined resource footprints. PROWESS is an edge testbed that answers this challenge. PROWESS provides access across an institution to sensors, compute resources, and software for testing constrained edge applications. PROWESS runs edge workloads as sets of containers with access to sensors and specialized hardware on an expandable cluster of light-weight edge nodes which leverage institutional networks to decrease implementation cost and provide wide access to sensors. We implemented a multi-node PROWESS deployment connected to sensors across Ohio State University’s campus. Using three edge-native applications, we demonstrate that PROWESS is simple to configure, has a small resource footprint, scales gracefully, and minimally impacts institutional networks. We also show that PROWESS closely approximates native execution of edge workloads and facilitates experiments that other systems testbeds can not.
ITSM in Supercomputing: Improving service delivery, reliability, and user satisfaction

Supercomputing in small academic centers has traditionally been driven by informal “get it done” practices. As environments grow more complex and user needs become more diverse, Information Technology Service Management (ITSM) practices are a valuable tool for formalizing processes. Implementing ITSM practices allows centers to better manage risk, increase stability, and better record data about changes and support needs to further the science mission of the clients more effectively. Additionally, the maturity of ITSM practices within University IT (Information Technology) has advanced the expectation that supercomputer centers work this way. In this paper we will describe the ongoing ITSM implementations at the Ohio Supercomputer Center (OSC) and explain the challenges and benefits we have seen.
Adapting novelty towards generating antigens for antivirus systems

It is well known that anti-malware scanners depend on malware signatures to identify malware. However, even minor modifications to malware code structure results in a change in the malware signature thus enabling the variant to evade detection by scanners. Therefore, there exists the need for a proactively generated malware variant dataset to aid detection of such diverse variants by automated antivirus scanners. This paper proposes and demonstrates a generic assembly source code based framework that facilitates any evolutionary algorithm to generate diverse and potential variants of an input malware, while retaining its maliciousness, yet capable of evading antivirus scanners. Generic code transformation functions and a novelty search supported quality metric have been proposed as components of the framework to be used respectively as variation operators and fitness function, for evolutionary algorithms. The results demonstrate the effectiveness of the framework in generating diverse variants and the generated variants have been shown to evade over 98% of popular antivirus scanners. The malware variants evolved by the framework can serve as antigens to assist malware analysis engines to improve their malware detection algorithms.
Comprehensive Analyses of miRNAs Revealed miR-92b-3p, miR-182-5p and miR-183-5p as Potential Novel Biomarkers in Melanoma-Derived Extracellular Vesicles

Extracellular vesicles (EVs) are important mediators in the intercellular communication, influencing the function and phenotype of different cell types within the tumor micro-milieu and thus promote tumor progression. Since EVs safely transport packages of proteins, lipids and also nucleic acids such as miRNAs, EVs and their cargo can serve as diagnostic and prognostic markers. Therefore, the aim of this study was to investigate EV embedded miRNAs specific for melanoma, which could serve as potential biomarkers. In contrast to previous studies, we not only analysed miRNAs from EVs, but also included the miRNA profiles from the EV-secreting cells to identify candidates as suitable biomarkers. While the characterization of EVs derived from normal melanocytes and melanoma cells showed largely comparable properties with regard to size distribution and expression of protein markers, the NGS analyses yielded marked differences for several miRNAs. While miRNA load of EVs derived from normal human epidermal melanocytes (NHEMs) and melanoma cells were very similar, they were highly different from their secreting cells. By comprehensive analyses, six miRNAs were identified to be enriched in both melanoma cells and melanoma cell-derived EVs. Of those, the accumulation of miR-92b-3p, miR-182-5p and miR-183-5p in EVs could be validated in vitro. By functional network generation and pathway enrichment analysis we revealed an association with different tumor entities and signaling pathways contributing melanoma progression. Furthermore, we found that miR-92b-3p, miR-182-5p and miR-183-5p were also enriched in EVs derived from serum of melanoma patients. Our results support the hypothesis that miRNAs derived from EVs can serve as prognostic or diagnostic liquid biopsy markers in melanoma. We identified EV-derived miRNAs and showed that those miRNAs, which were enriched in melanoma cells and EVs, are also found elevated in serum-derived EVs of patients with metastatic melanoma, but not in healthy subjects.
Regional Patterning of Adult Neurogenesis in the Homing Pigeon’s Brain

In the avian brain, adult neurogenesis has been reported in the telencephalon of several species, but the functional significance of this trait is still ambiguous. Homing pigeons (Columba livia f.d.) are well-known for their navigational skills. Their brains are functionally adapted to homing with, e.g., larger hippocampi. So far, no comprehensive mapping of adult neuro- and gliogenesis or studies of different developmental neuronal stages in the telencephalon of homing pigeons exists, although comprehensive analyses in various species surely will result in a higher understanding of the functional significance of adult neurogenesis. Here, adult, free flying homing pigeons were treated with 5-bromo-deoxyuridine (BrdU) to label adult newborn cells. Brains were dissected and immunohistochemically processed with several markers (GFAP, Sox2, S100ß, Tbr2, DCX, Prox1, Ki67, NeuN, Calbindin, Calretinin) to study different stages of adult neurogenesis in a quantitative and qualitative way. Therefore, immature and adult newborn neurons and glial cells were analyzed along the anterior–posterior axis. The analysis proved the existence of different neuronal maturation stages and showed that immature cells, migrating neurons and adult newborn neurons and glia were widely and regionally unequally distributed. Double- and triple-labelling with developmental markers allowed a stage classification of adult neurogenesis in the pigeon brain (1: continuity of stem cells/proliferation, 2: fate specification, 3: differentiation/maturation, 4: integration). The most adult newborn neurons and glia were found in the intercalated hyperpallium (HI) and the hippocampal formation (HF). The highest numbers of immature (DCX+) cells were detected in the nidopallium (N). Generally, the number of newborn glial cells exceeded the number of newborn neurons. Individual structures (e.g., HI, N, and HF) showed further variations along the anterior–posterior axis. Our qualitative classification and the distribution of maturing cells in the forebrain support the idea that there is a functional specialization, respectively, that there is a link between brain-structure and function, species-specific requirements and adult neurogenesis. The high number of immature neurons also suggests a high level of plasticity, which points to the ability for rapid adaption to environmental changes through additive mechanisms. Furthermore, we discuss a possible influence of adult neurogenesis on spatial cognition.
Analysis of Enterprise Social Responsibility to Employee Psychological Satisfaction Based on Discriminant Least Square Regression

Employee psychological satisfaction is the satisfaction of perception of environmental factors at the psychological and physiological levels, that is, the employees’ subjective response to the work situation. How to enhance employee loyalty and psychological satisfaction has always been a hot issue in theoretical and practical research. With the development of artificial intelligence (AI), many AI methods are widely used to find important factors which have significant influences on the psychological satisfaction of employees. Feature selection methods as one kind of AI models can select discriminant features which have high correlation with the outcome. In this study, we first construct 19 factors from enterprise social responsibility. Then we use a discriminant least square regression model to select most relative factors associating with employee psychological satisfaction. Our experimental results show that the psychological satisfaction of employees is very related to salary, security, welfare, occupational health, and fairness. In addition, we find that discriminant least square regression performs better than the comparison feature selection methods we select, and the selected factors are more in line with our perceptions and expectations.