Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-Disturbance Control for Nonlinear Systems Based on Interval Observer

This paper proposes a disturbance interval estimation approach for nonlinear systems. Distinct from the conventional estimation schemes aiming to estimate the magnitude of the disturbance, the disturbance interval estimation approach is capable of approximating the time-varying upper and lower boundaries for the disturbance. To achieve this, the dynamics of the boundary estimation errors are constructed as positive systems by introducing a time-invariant coordinate transformation. The boundary estimation errors are guaranteed to be nonnegative and bounded. Then, the generated interval is capable of covering all possible values of the current disturbance and the interval width can provide a quantized guidance on the estimation accuracy at any time. Furthermore, a general antidisturbance control scheme is summarized in light of the proposed disturbance interval estimation approach. As an illustrative case, an antidisturbance control method is developed by integrating the proposed estimation approach and the sliding mode control. Finally, the proposed disturbance interval estimation approach and the anti-disturbance control method are demonstrated through both the numerical and the experimental validation.
Passive and Context-Aware In-Home Vital Signs Monitoring Using Co-Located UWB-Depth Sensor Fusion

Basic vital signs such as heart and respiratory rates (HR and RR) are essential bio-indicators. Their longitudinal in-home collection enables prediction and detection of disease onset and change, providing for earlier health intervention. In this article, we propose a robust, non-touch vital signs monitoring system using a pair of co-located Ultra-Wide Band (UWB) and depth sensors. By extensive manual examination, we identify four typical temporal and spectral signal patterns and their suitable vital sign estimators. We devise a probabilistic weighted framework (PWF) that quantifies evidence of these patterns to update the weighted combination of estimator output to track the vital signs robustly. We also design a “heatmap”-based signal quality detector to exclude the disturbed signal from inadvertent motions. To monitor multiple co-habiting subjects in-home, we build a two-branch long short-term memory (LSTM) neural network to distinguish between individuals and their activities, providing activity context crucial to disambiguating critical from normal vital sign variability. To achieve reliable context annotation, we carefully devise the feature set of the consecutive skeletal poses from the depth data, and develop a probabilistic tracking model to tackle non-line-of-sight (NLOS) cases. Our experimental results demonstrate the robustness and superior performance of the individual modules as well as the end-to-end system for passive and context-aware vital sign monitoring.
Extending the push and pull search framework with boundary search for constrained multi-objective optimization

Constraints are a reality in many real-world application areas, adding feasibility to the existing multiple objective challenge. Further, the presence of complex constraints poses a significant challenge to multi-objective evolutionary algorithms. A recently proposed biphasic multi-objective evolutionary framework for constrained multi-objective optimization problems is the Push and Pull Search framework. This framework benefits from a strong exploration of the constrained landscape during the search for the unconstrained Pareto-Front during the first phase. The work herein extends the Push and Pull Search framework, extending landscape information gathering in the push phase; adding a binary search of the feasible and infeasible regions and creating a suitably diverse population and improved initialization for the push phase.
Opportunities for genetic improvement of cryptographic code

Cryptography is one of the main tools underlying the security of our connected world. Cryptographic code must achieve both high security requirements and high performance. Automatic generation and genetic improvement of such code are underexplored, making cryptographic code a prime target for future research. With the proliferation of computers into all aspects of human life, the amount of sensitive data being processed keeps increasing. As cryptography is one of the main tools underlying the security of our modern and connected world, cryptographic software must meet not only high security requirements, but also exhibit excellent nonfunctional properties, such as high performance and low energy consumption. Hence, we see cryptography as a prime target domain for single- and multi-objective code optimization.
Chaos engineering: stress-testing algorithms to facilitate resilient strategic military planning

We present our Chaos Engineering framework that accelerates the emergence of resilient strategic plans to maintain high levels of performance given the turbulent and unpredictable conditions in real-world military deployments. Our proposed Chaos Engine will stress agents by intelligently searching over scenario chaos factors reflecting real-world events such as weather changes, loss of communication, basing and access, platform failures, and enemy force composition and mission plans. Our work is split into four technical phases: 1) Evaluate: model and characterize agent performance in adaptively generated simulation environments; 2) Challenge: systematically find chaos factors that expose failures; 3) Fitness: balance challenge and feasibility; and 4) Diversity: maintain a population of scenarios that expose the agent to a range of challenges. The work presented here focuses on the Challenge phase and provides preliminary results for our adaptive domain generation paradigm, known as the adversarial architect. The adversarial architect will use an evolutionary-based optimizer to generate diverse scenarios that start simple and become more challenging as agents learn. We demonstrate the efficacy of our approach in the Acrobot domain, where we show the adversarial architect generating an Auto Curricula of inertial parameters that effectively guides the learning agent to resilient and effective policies.
Feature encoding with autoencoder and differential evolution for network intrusion detection using machine learning

With the increasing use of computer networks and distributed systems, network security and data privacy are becoming major concerns for our society. In this paper, we present an approach based on an autoencoder trained with differential evolution for feature encoding of network data with the goal of improving security and reducing data transfers. One of the novel elements used in differential evolution for intrusion detection is the enhancements in the fitness function by adding the performance of a machine learning algorithm. We conducted an extensive evaluation of six machine learning algorithms for network intrusion detection using encoded data from well-known publicly available network datasets UNSW-NB15. The experiments clearly showed the supremacy of random forest, support vector machine, and K-nearest neighbors in terms of accuracy, and this was not affected to a high degree by reducing the number of features. Furthermore, the machine learning algorithm that was used during training (Linear Discriminant Analysis classifier) got a 14 percentage points increase in accuracy. Our results also showed clear improvements in execution times in addition to the obvious secure aspects of encoded data. Additionally, the performance of the proposed method outperformed one of the most commonly used feature reduction methods, Principal Component Analysis.
Benchmarking ϵMAg-ES and BP-ϵMAg-ES on the bbob-constrained testbed

The ϵMAg Evolution Strategie and its bi-population version (BP-ϵMAg-ES), which performed well on the CEC 2018 and CEC 2020 benchmark sets, are evaluated using the COCO bbob-constrained 2.6. 2 benchmark release. Furthermore, the performance of Matlab's fmincon is considered as a baseline algorithm. Unlike the results of the CEC competitions, the outcomes obtained on the COCO 2.6. 2 test set do not reveal any advantage of using a bi-population approach. The reasons for these observations needs still to be investigated in future research.
Towards the evolutionary assessment of neural transformers trained on source code

In recent years, deep learning have become popular for solving tasks in a wide range of domains. With this growing diffusion, combined with architectures becoming increasingly complex and sophisticated, understanding how deep models make their predictions is now a crucial and challenging research issue. In this work, we are interested in analysing the behaviour of networks that deal with source code, and we address the problem along two experimental directions: in the first one, we study the activations of the neurons of a transformer trained in the detection of software vulnerabilities so as to identify if (and, eventually, where) some human understandable concepts emerge in the network. In the second one, we generate programs by applying a grammar-based evolutionary algorithm with a fitness function that favours individuals which stimulate (or weaken) the activations in neurons where given concepts majorly emerge. We then study the output of the network on sets of such evolved programs, i.e. how the evolutionary pressure along the direction of a concept affects the prediction. We finally discuss how this combination of evolutionary algorithms with fitness functions derived from the neural activations can be effective for explaining the decision process of deep models, and we suggest further research directions.
Exactly characterizable parameter settings in a crossoverless evolutionary algorithm

The number of offspring and the population size greatly influence the performance of the Plant Propagation Algorithm, a crossover-less metaheuristic, when applied to the Euclidean traveling salesman problem. However, with large volumes of experimental data, a clearly characterizable relation emerges, facilitating both an exact parameterization as well as opening up perspectives on a precisely predictable outcome. These results directly question its position between exact, approximation, and PTAS algorithms. Although the metaheuristic is thereby officially parameter sensitive when deployed to the Euclidean traveling salesman problem, it does appear to be largely insensitive to different problem instances. This is very different from earlier results on continuous optimization, which were completely opposite. Why is that, and what does it mean for our practices in algorithmic benchmarking?
Neuroevolution of recurrent architectures on control tasks

Modern artificial intelligence works typically train the parameters of fixed-sized deep neural networks using gradient-based optimization techniques. Simple evolutionary algorithms have recently been shown to also be capable of optimizing deep neural network parameters, at times matching the performance of gradient-based techniques, e.g. in reinforcement learning settings. In addition to optimizing network parameters, many evolutionary computation techniques are also capable of progressively constructing network architectures. However, constructing network architectures from elementary evolution rules has not yet been shown to scale to modern reinforcement learning benchmarks. In this paper we therefore propose a new approach in which the architectures of recurrent neural networks dynamically evolve according to a small set of mutation rules. We implement a massively parallel evolutionary algorithm and run experiments on all 19 OpenAI Gym state-based reinforcement learning control tasks. We find that in the majority of cases, dynamic agents match or exceed the performance of gradient-based agents while utilizing orders of magnitude fewer parameters. We believe our work to open avenues for real-life applications where network compactness and autonomous design are of critical importance. We provide our source code, final model checkpoints and full results at github.com/MaximilienLC/nra. Artificial neural networks are computing systems that have become central to the field of artificial intelligence. Through waves of innovation, these networks have gotten bigger, more efficient, and increasingly competent on a wide range of tasks. Most often, the parameters of these artificial neural networks are optimized using first-order gradient-based optimization techniques, yet their architecture is for the most part still constructed manually.
AutoMoDe-pomodoro: an evolutionary class of modular designs

In this paper, we reintroduce evolutionary algorithms into Auto-MoDe, an automatic design approach which optimizes behavioural modules into a probabilistic finite automaton. We evaluate three approaches, with different encodings of the probabilistic finite automaton phenotype, and observe their performances. This work opens modular designs to more advanced evolutionary robotics methods, such as novelty search and embodied evolution.
Towards landscape-aware parameter tuning for the (1 + (λ, λ)) genetic algorithm for permutations

The choice of parameter values greatly affects the performance of evolutionary algorithms. Many current parameter tuning approaches require multiple runs of the tuned algorithm, which makes it hard to use them in budget-constrained environments. Recently, an approach to parameter tuning on binary string problems was proposed, which uses machine learning and fitness landscape analysis to transfer knowledge on good parameter choices between similar problem instances. This approach allows using the performance data obtained on simple benchmark problems with different fitness landscape features to quickly choose suitable parameters for a given problem instance. In this paper, we aim to extend this approach to permutation-based problems by tuning the recently proposed version of the (1 + (λ, λ)) genetic algorithm for permutations-based problems. To do this, we develop a set of fitness landscape features that can be computed for permutations. We collect the algorithm's performance dataset on multiple instances of the W-model benchmark problem layered over the Ham problem for permutations. Finally, we present the preliminary experimental evaluation of the (1 + (λ, λ)) genetic algorithm tuned by the proposed approach.
Manufacturing and Sensorial Acceptance of Cereal Bars Enriched with Flaxseed (Linum usitatissimum) Flour

There is increasing demand from consumers for foods with functional properties. These foods, in addition to presenting adequate nutrient levels, provide beneficial health effects thus helping to prevent numerous diseases. Among the functional foods, the flaxseed (Linum usitatissimum), belonging to the family Linaceae, is of great nutritional value. This oleaginous is rich in proteins, lipids, lignans and fibers. Consumers have preference on cereal bars for being practical, healthy and with low calorie density when proper combinations of ingredients are used. This work had as objective the production and sensorial evaluation of cereal bars enriched with flaxseed flour. Two formulations, denominated S.01 and S.02, respectively added with corresponding 5% and 10% flaxseed flour, respectively, were prepared. The physico-chemical analyzes comprised moisture and ash contents. The cereal bars were sensorially evaluated by 50 untrained testers by using the nine-point hedonic scale including attributes of flavor, texture and overall acceptance, besides intention of purchase. The averages from the sensorial evaluation were between 7 and 8, indicating, with purchasing preference of about 90%, which was considered a high acceptance rate.
Constrained blackbox optimization with the NOMAD solver on the COCO constrained test suite

The context of this work is constrained blackbox optimization. It describes the mesh adaptive direct search (MADS) derivative-free optimization algorithm using the progressive barrier strategy to handle quantifiable and relaxable constraints. Through its implementation in the NOMAD solver, MADS is tested on the new bbob-constrained suite of analytical constrained problems from the COCO platform, and compared with the CMA-ES heuristic. Computational tests are illustrated with the postprocessing graphs from the COCO platform, as well as with data profiles, an established tool in the derivative-free optimization community, adapted here for the constrained case. The results illustrate that researchers must be very careful with the use of these tools, which are complementary and should ideally be used together. Their many variations may show different outcomes, and hence many graphs are necessary in order to provide the best overall view.
Benchmarking some variants of the CMAES-APOP using keeping search points and mirrored sampling combined with active CMA on the BBOB noiseless testbed

In this paper, we evaluate some variants of the CMAES-APOP using two additional methods. The first method is the combination of mirrored sampling with active covariance matrix adaptation. The second one is to keep some search points of the previous iteration for the evolution of the current iteration when the population size is large. Moreover, we propose a strategy to reduce the step-size when the population size attains its upper bound for several consecutive iterations. We will show that the techniques can enhance the performance on some multi-modal functions, especially on the weakly-structured multi-modal ones, and the overall performance in all dimensions.
Fitness diversification in the service of fitness optimization: a comparison study

Blindly chasing after fitness is not the best strategy for optimization of hard problems, as it usually leads to premature convergence and getting stuck in low-quality local optima. Several techniques such as niching or quality-diversity algorithms have been established that aim to alleviate the selective pressure present in evolutionary algorithms and to allow for greater exploration. Yet another group of methods which can be used for that purpose are fitness diversity methods. In this work we compare the standard single-population evolution against three fitness diversity methods: fitness uniform selection scheme (FUSS), fitness uniform deletion scheme (FUDS), and convection selection (ConvSel). We compare these methods on both mathematical and evolutionary design benchmarks over multiple parametrizations. We find that given the same computation time, fitness diversity methods regularly surpass the performance of the standard single-population evolutionary algorithm.
Colloquium : Weakly interacting, dilute Bose gases in 2D

This article surveys a number of theoretical problems and open questions in the field of twodimensional dilute Bose gases with weak repulsive interactions. In contrast to three dimensions, in two dimensions the formation of long-range order is prohibited by the Bogoliubov-Hohenberg theorem, and Bose-Einstein condensation is not expected to be realized. Nevertheless, the first experimental indications supporting the formation of a condensate in low dimensional systems have been recently obtained. This unexpected behaviour appears to be due to the non-uniformity introduced into a system by the external trapping potential. Theoretical predictions, made for homogeneous systems, require therefore careful reexamination. We survey a number of popular theoretical treatments of the dilute weakly interacting Bose gas and discuss their regions of applicability. The possibility of Bose-Einstein condensation in a two-dimensional gas, the validity of the perturbative t-matrix approximation and the diluteness condition are issues that we discuss in detail.
AbstractSwarm multi-agent logistics competition entry: QPlus

The AbstractSwarm Framework [1] was developed to study multi-agent simulation for optimizing logistics scenarios, with a special focus on (but not restricted to) hospital logistics. The basics of a solution to the accompanying competition [2] and the included benchmark problems is to be presented in this paper. To achieve this, Q-Learning [5, 7, 8] is used to determine scores for possible actions an agent can take depending on the state of the AbstractSwarm Graph representing the environment. Every action receives multiple such scores which are in turn aggregated, hence the name "QPlus".
Leveraging fuzzy system to reduce uncertainty of decision making in software engineering automation

Decision making in automated GI-based software engineering tasks can significantly affect the performance of the system. However, modern SE usually presents high uncertainty in such decision making process due to the existence of multiple solutions that reply on heuristics. We propose to apply the theory of Fuzzy System to obtain a final decision with lower uncertainty and higher accuracy. We also demonstrate a motivating example and discuss the challenges and opportunities for applying fuzzy system to SE tasks.
TaskFlow: An Energy- and Makespan-Aware Task Placement Policy for Workflow Scheduling through Delay Management

Datacenters need to become more power efficient for political and climate reasons. In this work, we introduce an idea for the community to further explore. We embed the idea in TaskFlow: a makespan conservative, energy-aware task placement policy for workflow scheduling. Using static, rough numbers and simulation, we obtain energy savings between [4.24, 47.00]% and [0.1, 13.6]%, respectively. We also present some pitfalls that should be investigated further, notably starvation of large tasks when using TaskFlow.
Recent Advances in Baggage Threat Detection: A Comprehensive and Systematic Survey

X-ray imagery systems have enabled security personnel to identify potential threats contained within the baggage and cargo since the early 1970s. However, the manual process of screening the threatening items is time-consuming and vulnerable to human error. Hence, researchers have utilized recent advancements in computer vision techniques, revolutionized by machine learning models, to aid in baggage security threat identification via 2D X-ray and 3D CT imagery. However, the performance of these approaches is severely affected by heavy occlusion, class imbalance, and limited labeled data, further complicated by ingeniously concealed emerging threats. Hence, the research community must devise suitable approaches by leveraging the findings from existing literature to move in new directions. Towards that goal, we present a structured survey providing systematic insight into state-of-the-art advances in baggage threat detection. Furthermore, we also present a comprehensible understanding of X-ray-based imaging systems and the challenges faced within the threat identification domain. We include a taxonomy to classify the approaches proposed within the context of 2D and 3D CT X-ray-based baggage security threat screening and provide a comparative analysis of the performance of the methods evaluated on four benchmarks. Besides, we also discuss current open challenges and potential future research avenues.