Miracle-dz/UniFAR-FTUs huggingface dataset:
Atrributed Graph Embedding Based on Multiobjective Evolutionary Algorithm for Overlapping Community Detection

Graph embedding methods aim to represent nodes in the network into a low-dimensional and continuous vector space while preserving the topological structure and varieties of relational information maximally. Nowadays the structural connections of networks and the attribute information about each node are more easily available than before. As a result, many community detection algorithms for attributed networks have been proposed. However, the majority of these methods cannot deal with the overlapping community detection problem, which is one of the most significant issues in the real-world complex network study. In addition, it is quite challenging to make full use of both structural and attribute information instead of only focusing on one part. To this end, in this paper we innovatively combine the graph embedding with multiobjective evolutionary algorithms (MOEAs) for overlapping community detection problems in attributed networks. As far as I am concerned, MOEA is first used to integrate with graph embedding methods for overlapping community detection. We term our method as MOEA-GEOV, which can automatically determine the number of communities without any prior knowledge and consider topological structure and vertex properties synchronously. In MOEA-GEOV, two objective functions concerning community structure and attribute similarity are carefully designed. Moreover, a heuristic initialization method is proposed to get a relatively good initial population. Then a novel encoding and decoding strategy is designed to efficiently represent the overlapping communities and corresponding embedded representation. In the experiments, the performance of MOEA-GEOV is validated on both single and multiple attribute real-world networks. The experimental results of community detection tasks demonstrate our method can effectively obtain overlapping community structures with practical significance.
A Programming Framework for Robot Swarms

Swarm robotics is a well-evolved research area over the past couple of decades. In this paper, we introduce a framework for programming swarm robots in a novel approach based on behaviour categorization, pheromone communication, and state representation. The framework is packaged with a set of well defined and tested behaviours that are structured based on the level of interactions between robots. These behaviours can be combined and integrated into the user code when implementing new behaviours. Robots function in a user-defined state and perform actions attributed to that state. We have used a behaviour- based bottom-up design approach for designing these behaviours which eventually alleviate the debugging and implementation process. The efficacy of our work is validated using results obtained from a custom-built simulation platform. These results include tests involving an random movement behaviour, an object finding behaviour, and implementation of an aggregation behaviour.
Learning Bayesian Networks Structures with an Effective Knowledge-driven GA

Bayesian networks (BNs) are probabilistic graphical models, which are regarded as one of the most effective theoretical models in the field of representing and reasoning under uncertainty. Learning BNs structure is an NP-hard problem since the search space of structure grows super-exponentially as the increasing of the number of variables. Evolutionary algorithms (EAs) are widely used to learn BNs structure while singlesolution searching methods may trap into local optima. This work aims to propose an efficient knowledge-driven Genetic algorithm (EKGA-BN) to solve the BN structure learning problem. The proposed EKGA-BN uses a novel selection operator to keep population diversity in order to learn a BN structure with higher accuracy. The idea of Hill climbing algorithm (HC) is combined in the selection operator so as to accelerate the convergence rate. A novel knowledge-driven mutation procedure is proposed to enhance the local search ability of EKGA-BN. Experimental results on four well-known benchmark networks show that the proposed method outperforms state-of-the-art algorithms in both convergence rate and the accuracy of BNs structure.
Simulator-Based Framework towards Improved Cache Predictability for Multi-Core Avionic Systems

Avionics are highly regulated systems and their criticality requires determinism. Single-core systems have a single processor running, hence only one execution flow, making them easily predictable in execution. However, manufacturers are moving towards multi-core processors manufacturing only. Avionics systems are thus compelled to transition from single-core to multi-core architectures. Mechanisms to mitigate the loss of determinism when using multi-core architectures must be developed to take profit from the parallelism offered by them. This work focuses on partitioned avionics systems and more precisely Real-Time Operating Systems (RTOS) in avionics. We propose a trace-based framework that analyzes application be-havior. It also gives cache information through an integrated cache simulator. To validate our framework, we integrated a cache locking selection algorithm. When running tests through our framework and its embedded simulator, we have an improvement in execution predictability and a reduction of interference (over 25%) after locking data in the cache.
Surrogate-Assisted Memetic Algorithm with Adaptive Patience Criterion for Computationally Expensive Optimization

Surrogate-assisted memetic algorithm (SAMA) has been recognized to be an effective tool for computationally expensive optimization. The termination criterion of the local search in SAMA determines the allocation of limited computational resources between global and local search, and has a tremendous impact on the optimization performance. The commonly used termination criterion based on setting a limit to the local search depth can lead to premature termination or excessive stagnation iterations of the local search. This paper proposes a SAMA with adaptive patience criterion (SAMA/APC) to improve the efficiency of traditional SAMA. The SAMA/APC consists of three main subprocedures, which are carried out iteratively. First, the operators of differential evolution (DE) are employed for global exploration. Then, the proposed Kriging-based patience allocation strategy (KPAS) is performed, which adaptively allocates a patience value to each individual of the population according to two basic principles. Third, the trust-region search (TRS) is carried out on each individual for local exploitation. The TRS is a process of consuming the patience, and it terminates when the patience value is reduced to zero. The local optimum obtained by the TRS is returned back to the population of DE in the spirit of Lamarckian learning. Experimental studies on the CEC’ 14 expensive optimization test suite demonstrate the efficiency of the proposed SAMA/APC.
A Genetic Algorithm for the Thief Orienteering Problem

This paper approaches the Thief orienteering Problem, a multi-component problem that combines two combinatorial problems: orienteering Problem (OP) and Knapsack Problem (KP). In this problem, a person (called thief) has a capacitated knapsack and has a time limit to collect objects distributed in a set of points. The departure and arrival points are fixed. The thief begins his journey with an empty knapsack and travels with speed inversely proportional to the weight of the knapsack. As long as he has time, the thief can go through the points collecting the objects. The objective of the problem is to define which route and which objects the thief must collect to maximize the profit of the knapsack. We developed a heuristic algorithm based on the genetic algorithm (GA) metaheuristic and computational experiments were carried out in order to compare the performance of the developed algorithm with the existing algorithms in the literature. Our results showed that our GA was superior in the majority of the cases.
CHIF: Convoluted Histogram Image Features for Detecting Silicone Mask based Face Presentation Attack

Face recognition algorithms are generally vulnerable towards presentation attacks ranging from cost-effective ways such as print and replay to sophisticated mediums such as silicone masks. Carefully designed silicone masks have real-life face texture once wore and can exhibit facial motions; thereby making them challenging to detect. In the literature, while several algorithms have been developed for detecting print and replay based attacks, limited work has been done for detecting silicone mask-based attack. In this research, we propose a computationally efficient solution by utilizing the power of CNN filters, and texture encoding for silicone mask based presentation attacks. The proposed framework operates on the principle of binarizing the image region after convolving the region with the filters learned via CNN operations. On the challenging silicon mask face presentation attack database (SMAD), the proposed feature descriptor shows 3.8% lower error rate than the state-of-the-art algorithms.
Behavior of Bioinspired Algorithms in Parallel Island Models

Parallel island models are used to increase accuracy and performance (speed-up) of meta-heuristics. Such models provide gains by the exchange of information between islands through the migratory process. The key to obtaining gains with parallel island models is the manipulation of migration parameters, since depending on how these parameters are handled the gains vary. Based on this assumption, this work uses three meta-heuristics: genetic algorithm, self-adjusting particle swarm optimization and social spider algorithm. From each metaheuristic, parallel island models were proposed, diversifying the number of natives on the islands, and the behavior of these models were studied. The assessment confirmed the impact of variations migration parameters on accuracy and performance as well as the importance on the number of natives located on the islands. The best solutions were obtained with island models from genetic algorithm and self-adjusting particle swarm optimization, and the best speedups were achieved with island models from social spider algorithm.
Exploring Problem State Transformations to Enhance Hyper-heuristics for the Job-Shop Scheduling Problem

This study presents an offline learning Simulated Annealing approach to generate a constructive hyper-heuristic evaluated through training and testing on a set of instances for solving the Job-Shop Scheduling problem. The generated hyperheuristic uses a range of state features to control a set of low-level constructive heuristics. A hyper-heuristic is represented in terms of a set of rules, where each rule contains a fixed set of values for the features in consideration and the low level heuristic to be invoked. At each constructive step, the ‘closest’ rule is selected and then the corresponding constructive low level heuristic is applied. Our distance metric is the Euclidean distance between the values within the rule and the state features characterising the partial schedule along with the remaining jobs to be scheduled for the partial solution. In this paper, we study a set of features computed with various well-known metrics and different feature transformation methods for improving the characterization of the problem instances and solutions to Job-Shop Scheduling as a part of our approach. Eight different scenarios are evaluated on a set of randomly generated problem instances. Each scenario represents a distinct approach combining a different feature transformation applied during the training and testing phases. The empirical results show that transformations can improve the spread of feature values and the choice of the transformation methods is influential on the performance of the overall approach. A particular choice generates a slightly better performance when compared to the standard approach, which uses the original features at all times, indicating the potential of the proposed approach for the future studies.
480-GHz Sensor With Subharmonic Mixer and Integrated Transducer in a 130-nm SiGe BiCMOS Technology

A 480-GHz sensor consists of signal stimulus and the transducer element as well as a subharmonic mixer in a 130-nm SiGe BiCMOS technology is reported. It features a mixer-first architecture based on down-conversion subharmonic mixer, an local oscillator (LO) chain at 240-GHz using a frequency doubler with variable-gain characterization, and a 480-GHz RF chain, making the fully integrated 480-GHz receiver possible. In a frequency range of 210–270 GHz at a maximum of 1.5-V supply offset, the LO chain has a 14-dB power-level variation, comprising with a 120-GHz frequency quadrupler, a power amplifier, and a variable frequency doubler. The proposed subharmonic receiver is driven by the RF and LO chain with a multiplier factor of 16 and 8, respectively. In this way, 480-GHz signal is generated, fed through the transducer, and hetero-mixed at subharmonic mixer. The measured output power difference is adjustable over 8 dB. Along with the intermediate frequency (IF) bandwidth of 20 GHz, the wide RF bandwidth makes it suitable for submillimetre-wave receiver-based dielectric spectroscopy applications. The chip occupies an area of 2.2 mm2 and consumes 290 mW.
Identifying Parkinson’s Disease Through the Classification of Audio Recording Data

Developments in artificial intelligence can be leveraged to support the diagnosis of degenerative disorders, such as epilepsy and Parkinson’s disease. This study aims to provide a software solution, focused initially towards Parkinson’s disease, which can positively impact medical practice surrounding degenerative diagnoses. Through the use of a dataset containing numerical data representing acoustic features extracted from an audio recording of an individual, it is determined if a neural approach can provide an improvement over previous results in the area. This is achieved through the implementation of a feedforward neural network and a layer recurrent neural network. By comparison with the state-of-the-art, a Bayesian approach providing a classification accuracy benchmark of 87.1%, it is found that the implemented neural networks are capable of average accuracy of 96%, highlighting improved accuracy for the classification process. The solution is capable of supporting the diagnosis of Parkinson’s disease in an advisory capacity and is envisioned to inform the process of referral through general practice.
Adaptive Dynamic Surface Control for Active Suspension With Electro-Hydraulic Actuator Parameter Uncertainty and External Disturbance

In this paper, a new adaptive dynamic surface control strategy is proposed to deal with the uncertainty parameters of electro-hydraulic actuator and unknown external disturbances of vehicle active suspension system. A dynamic model of nonlinear vehicle active suspension is established. The adaptive parameter estimation laws are designed to estimate the uncertain parameters caused by the change of the physical characteristics of the electro-hydraulic actuator. In other words, the estimation of uncertain parameters in hydraulic system is beneficial to improve the control precision of vehicle. Nonlinear robust feedback signal is introduced to suppress unknown external disturbances. Then, this paper proposes an idea of dynamic surface in order to avoid explosion of complexity that caused by virtual control signal. The dynamic surface function replaces the derivative of virtual control signal in backstepping control, which reduces the computational complexity and improves the riding comfort. The simulation comparison shows that the presented method is effective in improving riding comfort and driving safety.
Shrinking Counterexamples in Property-Based Testing with Genetic Algorithms

In this paper, genetic algorithms are proposed to shrink counterexamples found by QuickChick, a property-based testing framework for Coq. In order to make the outcome of property-based testing humanly understandable and inspectable, genetic algorithms are brought into the realm of rigorous software development as shrinkers capable of handling a broad range of data structures. In the present study, two showcases, merge sort and insertion of red-black trees, are investigated for illustrative purposes. Due to the lack of relevant results existing in the literature, two baseline methods, random sample and random walk are included in the experiments for comparison with the proposed genetic algorithm. The obtained results indicate that the proposal is effective since the program mistake can be identified with ease by examining the shrunk counterexamples and also that the adopted genetic algorithm statistically significantly outperforms random sample and random walk in both counterexample sizes and running time.
Dynamic Weighted Heuristic Trust Path Search Algorithm

Trust propagation is being increasingly adopted to assist recommendation systems in providing more reliable information, upon which, users can make more accurate decisions. Optimal trust path search integrating trust value and path length plays a critical role in trust propagation, but suffers from insufficient performance regarding search accuracy and time. Generally, the quality of trust propagation is affected by the path length, and the longer the path is, the worse the trust quality is. However, the length of the path is not the unique crucial factor. Some longer paths with greater trust values may be more credible. In addition, the <inline-formula> <tex-math notation="LaTeX">$A^{\ast }$ </tex-math></inline-formula> algorithm can find an optimal solution, but it expends much time to distinguish some similar paths. The <inline-formula> <tex-math notation="LaTeX">$A^{\ast }$ </tex-math></inline-formula> algorithm is improved and a dynamic weighted heuristic trust path search algorithm is proposed. According to the six-degree space theory, the paths are extended to six-degree admissible trust paths. Then, according to the depths of the nodes in the search path, it relaxes the evaluation function <inline-formula> <tex-math notation="LaTeX">$f(n)$ </tex-math></inline-formula> by devising a dynamic weighted factor <inline-formula> <tex-math notation="LaTeX">$w$ </tex-math></inline-formula>, inserts all nodes satisfied specific conditions into the FOCAL list. Furthermore, it sets the secondary heuristic factor and selects the nodes with the minimum heuristic factor value to reach the target node, and outputs the optimal trust path. Experiments on the public Advogato and FilmTrust datasets demonstrated that the proposed algorithm could efficiently identify the reliable trust paths and predict trust value with high accuracy and reduced computational complexity. The proposed algorithm could be applied to recommendation systems in the future.
Transfer Learning Layer Selection Using Genetic Algorithm

The performance of transfer learning in convolutional neural networks depends on the selection of which layers are to be learned again and which are not. Generally, layers selection is performed manually; however, as the number of layers increases, the layers selection process becomes increasingly difficult. Thus, we propose a method to select an effective layers in transfer learning automatically using a genetic algorithm. In the proposed method, a genotype representing which layers’ weights are updated or fixed in transfer learning is considered, and we achieve efficient layers selection in the way that a genotype with high validation accuracy is survived during genotype selection. Experiments are performed using the InceptionV3 network that pre-trained ImageNet as source images with transfer learning to CIFAR-100 as target images. Experimental results demonstrate that the test data accuracy in an ensemble of models whose layers are selected by the genetic algorithm is 15% and 12% greater than that of models trained by from-scratch and fine-tuning, respectively. In general transfer learning approach, layers on the output side are selected as adjustable layers; however, it is found that the distribution of the selected layers as an effective adjustable layers obtained by the genetic algorithm extends to the entire network. Transfer learning using a genetic algorithm may successfully capture the characteristics of a convolutional neural network’s structure.
Involvement of Arginase in Methyl Jasmonate–induced Tomato Fruit Chilling Tolerance

A BSTRACT . The physiological role of arginase in nitrogen remobilization processes from protein degradation during seed germination has well been described in several species. However, very little is known about its possible roles in plant stress responses. Treatment of tomato fruit ( Solanum lycopersicum L.) with 0.05 m M methyl jasmonate (MeJA) enhanced transcription levels of arginase genes, especially LeARG2 . Chilling injury (CI) of fruit treated with 0.05 m M MeJA for 12 hours was also effectively alleviated, as manifested by decreases in CI index, electrolyte leakage, and malondialdehyde (MDA) content. To investigate the potential role of arginase in MeJA-induced chilling tolerance, fruit were treated with MeJA or the arginase inhibitor N v -hydroxy-nor- L -arginine (nor-NOHA) combined with MeJA and then stored at 2 8 C for 28 days. MeJA-induced arginase activity was strongly inhibited and the reduction of CI by MeJA was nearly abolished by the inhibitor. In addition, MeJA treatment increased the activities of superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX); inhibited peroxidase (POD) activities; and promoted proline and polyamines accumulation. These effects were partially counteracted by nor-NOHA; putrescine accumulation, however, was unaffected by the inhibitor. Our results indicate that arginase may be involved in MeJA-induced chilling tolerance, possibly by ameliorating the antioxidant enzyme system of fruit and increasing proline levels.
Automated, Reactive Pruning of System Entity Structures for Simulation Engineering

System Entity Structures (SES) are used to define families of systems. In this context they are employed in combination with a model base (MB) to describe a set of simulation models. Using a framework, simulation models are generated from the SES/MB in a goal-oriented manner, executed and their results analyzed. The entire process is automated, iterative and reactive. A concrete system variant in the family of systems is derived by pruning an SES. For the process of automated model generation, it is necessary to provide automatic pruning mechanisms. Especially for some types of nodes comprised in an SES, automatic pruning is a big challenge. Here, the challenges that arise when pruning SES containing hierarchies of multi-aspect and specialization nodes are analyzed and a solution is proposed.