| 1. |
- Bodén, Mikael, et al.
(author)
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Learning the Dynamics of Embedded Clauses
- 2003
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In: Applied intelligence (Boston). - : Springer Netherlands. - 0924-669X .- 1573-7497. ; 19:1-2, s. 51-63
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Journal article (peer-reviewed)abstract
- Recent work by Siegelmann has shown that the computational power of recurrent neural networks matches that of Turing Machines. One important implication is that complex language classes (infinite languages with embedded clauses) can be represented in neural networks. Proofs are based on a fractal encoding of states to simulate the memory and operations of stacks. In the present work, it is shown that similar stack-like dynamics can be learned in recurrent neural networks from simple sequence prediction tasks. Two main types of network solutions are found and described qualitatively as dynamical systems: damped oscillation and entangled spiraling around fixed points. The potential and limitations of each solution type are established in terms of generalization on two different context-free languages. Both solution types constitute novel stack implementations—generally in line with Siegelmann's theoretical work—which supply insights into how embedded structures of languages can be handled in analog hardware.
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| 2. |
- Brazier, Frances, et al.
(author)
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Compositional verification of a multi-agent system for one-to-many negotiation
- 2004
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In: Applied intelligence (Boston). - DORDRECHT : KLUWER ACADEMIC PUBL. - 0924-669X .- 1573-7497. ; 20:2, s. 95-117
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Journal article (peer-reviewed)abstract
- Verification of multi-agent systems hardly occurs in design practice. One of the difficulties is that required properties for a multi-agent system usually refer to multi-agent behaviour which has nontrivial dynamics. To constrain these multi-agent behavioural dynamics, often a form of organisational structure is used, for example, for negotiating agents, by following strict protocols. The claim is that these negotiation protocols entail a structured process that is manageable with respect to analysis, design and execution of such a multi-agent system. In this paper this is shown by a case study: verification of a multi-agent system for one-to-many negotiation in the domain of load balancing of electricity use. A compositional verification method for multi-agent systems is applied that allows to (1) logically relate dynamic properties of the multi-agent system as a whole to dynamic properties of agents, and (2) logically relate dynamic properties of agents to properties of their subcomponents. Given that properties of these subcomponents can be verified by more standard methods, these logical relationships provide proofs of the dynamic properties of the multi-agent system as a whole.
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| 3. |
- Daza, Iván García, et al.
(author)
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Sim-to-real transfer and reality gap modeling in model predictive control for autonomous driving
- 2023
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In: Applied Intelligence. - : Springer Science and Business Media LLC. - 1573-7497 .- 0924-669X. ; 53:10, s. 12719-12735
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Journal article (peer-reviewed)abstract
- The main challenge for the adoption of autonomous driving is to ensure an adequate level of safety. Considering the almost infinite variability of possible scenarios that autonomous vehicles would have to face, the use of autonomous driving simulators is becoming of utmost importance. Simulation suites allow the used of automated validation techniques in a wide variety of scenarios, and enable the development of closed-loop validation methods, such as machine learning and reinforcement learning approaches. However, simulation tools suffer from a standing flaw in that there is a noticeable gap between the simulation conditions and real-world scenarios. Although the use of simulators powers most of the research around autonomous driving, and is generally used within all domains it is divided into, there is an inherent source of error given the stochastic nature of activities performed in real world, which are unreplicable in computer environments. This paper proposes a new approach to assess the real-to-sim gap for path tracking systems. The aim is to narrow down the sources of error between simulation results and real-world conditions, and to evaluate the performance of the simulation suite in the design process by employing the information extracted from gap analysis, which adds a new dimension of development against other approaches for autonomous driving. A real-time model predictive controller (MPC) based on adaptive potential fields was developed and validated using the CARLA simulator. Both the path planning and vehicle control systems where tested in real traffic conditions. The error between the simulator and the real data acquisition was evaluated using the Pearson correlation coefficient (PCC) and the max normalized cross-correlation (MNCC). The controller was further evaluated on a process of sim-to-real transfer, and was finally tested both in simulation and real traffic conditions. A comparison was performed against an optimal-control ILQR-based model predictive controller was carried out to further showcase the validity of this approach.
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| 4. |
- Jabeen, Gul, et al.
(author)
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Machine learning techniques for software vulnerability prediction : a comparative study
- 2022
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In: Applied intelligence (Boston). - : SPRINGER. - 0924-669X .- 1573-7497.
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Journal article (peer-reviewed)abstract
- Software vulnerabilities represent a major cause of security problems. Various vulnerability discovery models (VDMs) attempt to model the rate at which the vulnerabilities are discovered in a software. Although several VDMs have been proposed, not all of them are universally applicable. Also most of them seldom give accurate predictive results for every type of vulnerability dataset. The use of machine learning (ML) techniques has generally found success in a wide range of predictive tasks. Thus, in this paper, we conducted an empirical study on applying some well-known machine learning (ML) techniques as well as statistical techniques to predict the software vulnerabilities on a variety of datasets. The following ML techniques have been evaluated: cascade-forward back propagation neural network, feed-forward back propagation neural network, adaptive-neuro fuzzy inference system, multi-layer perceptron, support vector machine, bagging, M5Rrule, M5P and reduced error pruning tree. The following statistical techniques have been evaluated: Alhazmi-Malaiya model, linear regression and logistic regression model. The applicability of the techniques is examined using two separate approaches: goodness-of-fit to see how well the model tracks the data, and prediction capability using different criteria. It is observed that ML techniques show remarkable improvement in predicting the software vulnerabilities than the statistical vulnerability prediction models.
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| 5. |
- Li, Zhongguo, et al.
(author)
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Detailed 3D human body reconstruction from multi-view images combining voxel super-resolution and learned implicit representation
- 2022
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In: Applied Intelligence. - : Springer Science and Business Media LLC. - 0924-669X .- 1573-7497. ; 52:6, s. 6739-6759
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Journal article (peer-reviewed)abstract
- The task of reconstructing detailed 3D human body models from images is interesting but challenging in computer vision due to the high freedom of human bodies. This work proposes a coarse-to-fine method to reconstruct detailed 3D human body from multi-view images combining Voxel Super-Resolution (VSR) based on learning the implicit representation. Firstly, the coarse 3D models are estimated by learning an Pixel-aligned Implicit Function based on Multi-scale Features (MF-PIFu) which are extracted by multi-stage hourglass networks from the multi-view images. Then, taking the low resolution voxel grids which are generated by the coarse 3D models as input, the VSR is implemented by learning an implicit function through a multi-stage 3D convolutional neural network. Finally, the refined detailed 3D human body models can be produced by VSR which can preserve the details and reduce the false reconstruction of the coarse 3D models. Benefiting from the implicit representation, the training process in our method is memory efficient and the detailed 3D human body produced by our method from multi-view images is the continuous decision boundary with high-resolution geometry. In addition, the coarse-to-fine method based on MF-PIFu and VSR can remove false reconstructions and preserve the appearance details in the final reconstruction, simultaneously. In the experiments, our method quantitatively and qualitatively achieves the competitive 3D human body models from images with various poses and shapes on both the real and synthetic datasets.
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| 6. |
- Markovic, Tijana, et al.
(author)
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Random forest with differential privacy in federated learning framework for network attack detection and classification
- 2024
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In: Applied intelligence (Boston). - : SPRINGER. - 0924-669X .- 1573-7497.
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Journal article (peer-reviewed)abstract
- Communication networks are crucial components of the underlying digital infrastructure in any smart city setup. The increasing usage of computer networks brings additional cyber security concerns, and every organization has to implement preventive measures to protect valuable data and business processes. Due to the inherent distributed nature of the city infrastructures as well as the critical nature of its resources and data, any solution to the attack detection calls for distributed, efficient and privacy preserving solutions. In this paper, we extend the evaluation of our federated learning framework for network attacks detection and classification based on random forest. Previously the framework was evaluated only for attack detection using four well-known intrusion detection datasets (KDD, NSL-KDD, UNSW-NB15, and CIC-IDS-2017). In this paper, we extend the evaluation for attack classification. We also evaluate how adding differential privacy into random forest, as an additional protective mechanism, affects the framework performances. The results show that the framework outperforms the average performance of independent random forests on clients for both attack detection and classification. Adding differential privacy penalizes the performance of random forest, as expected, but the use of the proposed framework still brings benefits in comparison to the use of independent local models. The code used in this paper is publicly available, to enable transparency and facilitate reproducibility within the research community.
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| 7. |
- Mousavi, Arash, et al.
(author)
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Ontology-driven coordination model for multiagent-based mobile workforce brokering systems
- 2012
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In: Applied intelligence (Boston). - : Springer Science and Business Media LLC. - 0924-669X .- 1573-7497. ; 36:4, s. 768-787
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Journal article (peer-reviewed)abstract
- Coordination has been recognized by many researchers as the most important feature of multi-agent systems. Coordination is defined as managing interdependencies amongst activities (Malone and Crowston in ACM Comput. Surv. 26(1):87-119, 1994). The traditional approach of implementing a coordination mechanism is to hard-wire it into a coordination system at design time. However, in dynamic and open environments, many attributes of the system cannot be accurately identified at the design time. Therefore, dynamic coordination, capable of coordinating activities at run-time, has emerged. On the other hand, a successful dynamic coordination model for multi-agent systems requires knowledge sharing as well as common vocabulary. Therefore, an ontological approach is an appropriate way in proposing dynamic coordination models for multi-agent systems. In this paper, an Ontology-Driven Dynamic Coordination Model (O-DC) for Multiagent-Based Mobile Workforce Brokering Systems (MWBS) (Mousavi et al. in Int. J. Comput. Sci. 6:(5):557-565, 2010; Mousavi et al. in Proceedings of 4th IEEE international symposium on information technology, ITSim'10, Kuala Lumpur, Malaysia, 15-17 June 2010, vol. 3, pp. 1416-1421, 2010; Mousavi and Nordin in Proceedings of the IEEE international conference on electrical engineering and informatics, Bandung, Indonesia, 17-19 June 2007, pp. 294-297, 2007) is proposed and formulated. Subsequently, the applicability of O-DC is examined via simulation based on a real-world scenario
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| 8. |
- Oyedotun, O. K., et al.
(author)
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Deep network compression with teacher latent subspace learning and LASSO
- 2020
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In: Applied intelligence (Boston). - : Springer Nature. - 0924-669X .- 1573-7497.
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Journal article (peer-reviewed)abstract
- Deep neural networks have been shown to excel in understanding multimedia by using latent representations to learn complex and useful abstractions. However, they remain unpractical for embedded devices due to memory constraints, high latency, and considerable power consumption at runtime. In this paper, we propose the compression of deep models based on learning lower dimensional subspaces from their latent representations while maintaining a minimal loss of performance. We leverage on the premise that deep convolutional neural networks extract many redundant features to learn new subspaces for feature representation. We construct a compressed model by reconstruction from representations captured by an already trained large model. As compared to state-of-the-art, the proposed approach does not rely on labeled data. Moreover, it allows the use of sparsity inducing LASSO parameter penalty to achieve better compression results than when used to train models from scratch. We perform extensive experiments using VGG-16 and wide ResNet models on CIFAR-10, CIFAR-100, MNIST and SVHN datasets. For instance, VGG-16 with 8.96M parameters trained on CIFAR-10 was pruned by 81.03 % with only 0.26 % generalization performance loss. Correspondingly, the size of the VGG-16 model is reduced from 35MB to 6.72MB to facilitate compact storage. Furthermore, the associated inference time for the same VGG-16 model is reduced from 1.1 secs to 0.6 secs so that inference is accelerated. Particularly, the proposed student models outperform state-of-the-art approaches and the same models trained from scratch.
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| 9. |
- Picazo-Sanchez, Pablo, 1985-, et al.
(author)
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Analysing the impact of ChatGPT in research
- 2024
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In: Applied intelligence (Boston). - New York, NY : Springer. - 0924-669X .- 1573-7497. ; 54:5, s. 4172-4188
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Journal article (peer-reviewed)abstract
- Large Language Models (LLMs) are a type of machine learning that handles a wide range of Natural Language Processing (NLP) scenarios. Recently, in December 2022, a company called OpenAI released ChatGPT, a tool that, within a few months, became the most representative example of LLMs, automatically generating unique and coherent text on many topics, summarising and rewriting it, or even translating it to other languages. ChatGPT originated some controversy in academia since students can generate unique text for writing assessments being sometimes extremely difficult to distinguish whether it comes from ChatGPT or a person. In research, some journals specifically banned ChatGPT in scientific papers. However, when used correctly, it becomes a powerful tool to rewrite, for instance, scientific papers and, thus, deliver researchers’ messages in a better way. In this paper, we conduct an empirical study of the impact of ChatGPT in research. We downloaded the abstract of over 45,000 papers from over 300 journals from Dec 2022 and Feb 2023 belonging to different research editorials. We use four of the most known ChatGPT detection tools and conclude that ChatGPT played a role in around 10% of the papers published in every editorial, showing that authors from different fields have rapidly adopted such a tool in their research. © The Author(s) 2024.
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| 10. |
- Seyed Jalaleddin, Mousavirad, et al.
(author)
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Human mental search : a new population-based metaheuristic optimization algorithm
- 2017
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In: Applied intelligence (Boston). - : Springer Nature. - 0924-669X .- 1573-7497. ; 47:3, s. 850-887
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Journal article (peer-reviewed)abstract
- Population-based metaheuristic algorithms have become popular in recent years with them getting used in different fields such as business, medicine, and agriculture. The present paper proposes a simple but efficient population-based metaheuristic algorithm called Human Mental Search (HMS). HMS algorithm mimics the exploration strategies of the bid space in online auctions. The three leading steps of HMS algorithm are: (1) the mental search that explores the region around each solution based on Levy flight, (2) grouping that determines a promising region, and (3) moving the solutions toward the best strategy. To evaluate the efficiency of HMS algorithm, some test functions with different characteristics are studied. The results are compared with nine state-of-the-art metaheuristic algorithms. Moreover, some nonparametric statistical methods, including Wilcoxon signed rank test and Friedman test, are provided. The experimental results demonstrate that the HMS algorithm can present competitive results compared to other algorithms.
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