| 1. |
- Ahmed, Tawsin Uddin, et al.
(författare)
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An Integrated Deep Learning and Belief Rule Base Intelligent System to Predict Survival of COVID-19 Patient under Uncertainty
- 2022
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Ingår i: Cognitive Computation. - : Springer. - 1866-9956 .- 1866-9964. ; 14:2, s. 660-676
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Tidskriftsartikel (refereegranskat)abstract
- The novel Coronavirus-induced disease COVID-19 is the biggest threat to human health at the present time, and due to the transmission ability of this virus via its conveyor, it is spreading rapidly in almost every corner of the globe. The unification of medical and IT experts is required to bring this outbreak under control. In this research, an integration of both data and knowledge-driven approaches in a single framework is proposed to assess the survival probability of a COVID-19 patient. Several neural networks pre-trained models: Xception, InceptionResNetV2, and VGG Net, are trained on X-ray images of COVID-19 patients to distinguish between critical and non-critical patients. This prediction result, along with eight other significant risk factors associated with COVID-19 patients, is analyzed with a knowledge-driven belief rule-based expert system which forms a probability of survival for that particular patient. The reliability of the proposed integrated system has been tested by using real patient data and compared with expert opinion, where the performance of the system is found promising.
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| 2. |
- Cavallo, Filippo, et al.
(författare)
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Development of a socially believable multi-robot solution from town to home
- 2014
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Ingår i: Cognitive Computation. - : Springer Science and Business Media LLC. - 1866-9956 .- 1866-9964. ; 6:4, s. 954-967
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Tidskriftsartikel (refereegranskat)abstract
- Technological advances in the robotic and ICT fields represent an effective solution to address specific societal problems to support ageing and independent life. One of the key factors for these technologies is that they have to be socially acceptable and believable to the end-users. This paper aimed to present some technological aspects that have been faced to develop the Robot-Era system, a multi-robotic system that is able to act in a socially believable way in the environments daily inhabited by humans, such as urban areas, buildings and homes. In particular, this paper focuses on two services-shopping delivery and garbage collection-showing preliminary results on experiments conducted with 35 elderly people. The analysis adopts an end-user-oriented perspective, considering some of the main attributes of acceptability: usability, attitude, anxiety, trust and quality of life.
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| 3. |
- Emruli, Blerim, et al.
(författare)
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Analogical mapping with sparse distributed memory : a simple model that learns to generalize from examples
- 2014
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Ingår i: Cognitive Computation. - : Springer Science and Business Media LLC. - 1866-9956 .- 1866-9964. ; 6:1, s. 74-88
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Tidskriftsartikel (refereegranskat)abstract
- We present a computational model for the analogical mapping of compositional structures that com- bines two existing ideas known as holistic mapping vec- tors and sparse distributed memory. The model enables integration of structural and semantic constraints when learning mappings of the type x_i → y_i and computing analogies x_j → y_j for novel inputs x_j. The model has a one-shot learning process, is randomly initialized and has three exogenous parameters: the dimensionality D of representations, the memory size S and the prob- ability χ for activation of the memory. After learning three examples the model generalizes correctly to novel examples. We find minima in the probability of generalization error for certain values of χ, S and the number of different mapping examples learned. These results indicate that the optimal size of the memory scales with the number of different mapping examples learned and that the sparseness of the memory is important. The optimal dimensionality of binary representations is of the order 10^4, which is consistent with a known analytical estimate and the synapse count for most cortical neurons. We demonstrate that the model can learn analogical mappings of generic two-place relationships and we calculate the error probabilities for recall and generalization.
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| 4. |
- Kootstra, Gert, et al.
(författare)
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Predicting Eye Fixations on Complex Visual Stimuli Using Local Symmetry
- 2011
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Ingår i: Cognitive Computation. - : Springer Science and Business Media LLC. - 1866-9956 .- 1866-9964. ; 3:1, s. 223-240
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Tidskriftsartikel (refereegranskat)abstract
- Most bottom-up models that predict human eye fixations are based on contrast features. The saliency model of Itti, Koch and Niebur is an example of such contrast-saliency models. Although the model has been successfully compared to human eye fixations, we show that it lacks preciseness in the prediction of fixations on mirror-symmetrical forms. The contrast model gives high response at the borders, whereas human observers consistently look at the symmetrical center of these forms. We propose a saliency model that predicts eye fixations using local mirror symmetry. To test the model, we performed an eye-tracking experiment with participants viewing complex photographic images and compared the data with our symmetry model and the contrast model. The results show that our symmetry model predicts human eye fixations significantly better on a wide variety of images including many that are not selected for their symmetrical content. Moreover, our results show that especially early fixations are on highly symmetrical areas of the images. We conclude that symmetry is a strong predictor of human eye fixations and that it can be used as a predictor of the order of fixation.
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| 5. |
- Pang, S., et al.
(författare)
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DCTGM : A Novel Dual-channel Transformer Graph Model for miRNA-disease Association Prediction
- 2022
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Ingår i: Cognitive Computation. - : Springer. - 1866-9956 .- 1866-9964.
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Tidskriftsartikel (refereegranskat)abstract
- Studies have shown that as non-coding RNAs, miRNAs regulate all levels of life activities and most pathological processes. Therefore, identifying disease-related miRNAs is essential for disease diagnosis and treatment. However, traditional biological experiments are highly uncertain and time-consuming. Hence, advanced intelligent computational models are needed to address this problem. We propose a dual-channel transformer graph model, named DCTGM, to learn multi-scale representations for miRNA-disease association prediction. Specifically, DCTGM includes a transformer encoder (TE) and GraphSAGE encoder (GE). The TE intensely captures the important interaction information between miRNA-disease pairs, and the GE aggregates multi-hop neighbor information of miRNA-disease association heterograph to enrich node features. Then, an attention module is proposed to aggregate the dual-channel interactive representations, and we adopt a multi-layer perceptron (MLP) to predict the miRNA-disease association scores. The fivefold cross-validation experimental results demonstrate that our proposed DCTGM achieves the AP of 92.735%, F1 of 84.430%, accuracy of 85.255%, and ROC of 93.012%. In addition, we conduct case studies on brain neoplasms, kidney neoplasms, and breast neoplasms. The extensive experiments show that the dbDEMC database validates 100% of the top 20 predicted miRNAs associated with these diseases. This model can effectively predict the potential mirNA-disease association. Experiments have shown that miRNA associated with a new disease can also be predicted. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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| 6. |
- Pavlopoulos, Ioannis, et al.
(författare)
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Distance from Unimodality for the Assessment of Opinion Polarization
- 2023
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Ingår i: Cognitive Computation. - : Springer Science and Business Media LLC. - 1866-9956 .- 1866-9964. ; 15:2, s. 731-738
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Tidskriftsartikel (refereegranskat)abstract
- Commonsense knowledge is often approximated by the fraction of annotators who classified an item as belonging to the positive class. Instances for which this fraction is equal to or above 50% are considered positive, including however ones that receive polarized opinions. This is a problematic encoding convention that disregards the potentially polarized nature of opinions and which is often employed to estimate subjectivity, sentiment polarity, and toxic language. We present the distance from unimodality (DFU), a novel measure that estimates the extent of polarization on a distribution of opinions and which correlates well with human judgment. We applied DFU to two use cases. The first case concerns tweets created over 9 months during the pandemic. The second case concerns textual posts crowd-annotated for toxicity. We specified the days for which the sentiment-annotated tweets were determined as polarized based on the DFU measure and we found that polarization occurred on different days for two different states in the USA. Regarding toxicity, we found that polarized opinions are more likely by annotators originating from different countries. Moreover, we show that DFU can be exploited as an objective function to train models to predict whether a post will provoke polarized opinions in the future.
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| 7. |
- Persson, Andreas, 1980-, et al.
(författare)
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Fluent human–robot dialogues about grounded objects in home environments
- 2014
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Ingår i: Cognitive Computation. - : Springer. - 1866-9956 .- 1866-9964. ; 6:4, s. 914-927
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Tidskriftsartikel (refereegranskat)abstract
- To provide a spoken interaction between robots and human users, an internal representation of the robots sensory information must be available at a semantic level and accessible to a dialogue system in order to be used in a human-like and intuitive manner. In this paper, we integrate the fields of perceptual anchoring (which creates and maintains the symbol-percept correspondence of objects) in robotics with multimodal dialogues in order to achieve a fluent interaction between humans and robots when talking about objects. These everyday objects are located in a so-called symbiotic system where humans, robots, and sensors are co-operating in a home environment. To orchestrate the dialogue system, the IrisTK dialogue platform is used. The IrisTK system is based on modelling the interaction of events, between different modules, e.g. speech recognizer, face tracker, etc. This system is running on a mobile robot device, which is part of a distributed sensor network. A perceptual anchoring framework, recognizes objects placed in the home and maintains a consistent identity of the objects consisting of their symbolic and perceptual data. Particular effort is placed on creating flexible dialogues where requests to objects can be made in a variety of ways. Experimental validation consists of evaluating the system when many objects are possible candidates for satisfying these requests.
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| 8. |
- Rachkovskij, Dmitri A.
(författare)
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Shift-Equivariant Similarity-Preserving Hypervector Representations of Sequences
- 2024
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Ingår i: Cognitive Computation. - : Springer. - 1866-9956 .- 1866-9964. ; 16, s. 909-923
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Tidskriftsartikel (refereegranskat)abstract
- Hyperdimensional Computing (HDC), also known as Vector-Symbolic Architectures (VSA), is a promising framework for the development of cognitive architectures and artificial intelligence systems, as well as for technical applications and emerging neuromorphic and nanoscale hardware. HDC/VSA operate with hypervectors, i.e., neural-like distributed vector representations of large fixed dimension (usually > 1000). One of the key ingredients of HDC/VSA are the methods for encoding various data types (from numeric scalars and vectors to graphs) by hypervectors. In this paper, we propose an approach for the formation of hypervectors of sequences that provides both an equivariance with respect to the shift of sequences and preserves the similarity of sequences with identical elements at nearby positions. Our methods represent the sequence elements by compositional hypervectors and exploit permutations of hypervectors for representing the order of sequence elements. We experimentally explored the proposed representations using a diverse set of tasks with data in the form of symbolic strings. Although we did not use any features here (hypervector of a sequence was formed just from the hypervectors of its symbols at their positions), the proposed approach demonstrated the performance on a par with the methods that exploit various features, such as subsequences. The proposed techniques were designed for the HDC/VSA model known as Sparse Binary Distributed Representations. However, they can be adapted to hypervectors in formats of other HDC/VSA models, as well as for representing sequences of types other than symbolic strings. Directions for further research are discussed.
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| 9. |
- Thill, Serge, et al.
(författare)
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Modeling the Development of Goal-Specificity in Mirror Neurons
- 2011
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Ingår i: Cognitive Computation. - : Springer. - 1866-9956 .- 1866-9964. ; 3:4, s. 525-538
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Tidskriftsartikel (refereegranskat)abstract
- Neurophysiological studies have shown that parietal mirror neurons encode not only actions but also the goal of these actions. Although some mirror neurons will fire whenever a certain action is perceived (goal-independently), most will only fire if the motion is perceived as part of an action with a specific goal. This result is important for the action-understanding hypothesis as it provides a potential neurological basis for such a cognitive ability. It is also relevant for the design of artificial cognitive systems, in particular robotic systems that rely on computational models of the mirror system in their interaction with other agents. Yet, to date, no computational model has explicitly addressed the mechanisms that give rise to both goal-specific and goal-independent parietal mirror neurons. In the present paper, we present a computational model based on a self-organizing map, which receives artificial inputs representing information about both the observed or executed actions and the context in which they were executed. We show that the map develops a biologically plausible organization in which goal-specific mirror neurons emerge. We further show that the fundamental cause for both the appearance and the number of goal-specific neurons can be found in geometric relationships between the different inputs to the map. The results are important to the action-understanding hypothesis as they provide a mechanism for the emergence of goal-specific parietal mirror neurons and lead to a number of predictions: (1) Learning of new goals may mostly reassign existing goal-specific neurons rather than recruit new ones; (2) input differences between executed and observed actions can explain observed corresponding differences in the number of goal-specific neurons; and (3) the percentage of goal-specific neurons may differ between motion primitives.
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| 10. |
- Wallenberg, Marcus, et al.
(författare)
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Embodied Object Recognition using Adaptive Target Observations
- 2010
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Ingår i: Cognitive Computation. - : Springer. - 1866-9956 .- 1866-9964. ; 2:4, s. 316-325
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we study object recognition in the embodied setting. More specifically, we study the problem of whether the recognition system will benefit from acquiring another observation of the object under study, or whether it is time to give up, and report the observed object as unknown. We describe the hardware and software of a system that implements recognition and object permanence as two nested perception-action cycles. We have collected three data sets of observation sequences that allow us to perform controlled evaluation of the system behavior. Our recognition system uses a KNN classifier with bag-of-features prototypes. For this classifier, we have designed and compared three different uncertainty measures for target observation. These measures allow the system to (a) decide whether to continue to observe an object or to move on, and to (b) decide whether the observed object is previously seen or novel. The system is able to successfully reject all novel objects as “unknown”, while still recognizing most of the previously seen objects.
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