Expressive Cognitive Architecture for a Curious Social Robot

2021 ◽  
Vol 11 (2) ◽  
pp. 1-25
Author(s):  
Maor Rosenberg ◽  
Hae Won Park ◽  
Rinat Rosenberg-Kima ◽  
Safinah Ali ◽  
Anastasia K. Ostrowski ◽  
...  
Keyword(s):  
Developmental Psychology ◽  
State Of The Art ◽  
Cognitive Architecture ◽  
Autonomous Robot ◽  
Social Robots ◽  
Comparison Study ◽  
Daily Lives ◽  
New Approach ◽  
Integrate State ◽  
Learning Progress

Artificial curiosity, based on developmental psychology concepts wherein an agent attempts to maximize its learning progress, has gained much attention in recent years. Similarly, social robots are slowly integrating into our daily lives, in schools, factories, and in our homes. In this contribution, we integrate recent advances in artificial curiosity and social robots into a single expressive cognitive architecture. It is composed of artificial curiosity and social expressivity modules and their unique link, i.e., the robot verbally and non-verbally communicates its internally estimated learning progress, or learnability, to its human companion. We implemented this architecture in an interaction where a fully autonomous robot took turns with a child trying to select and solve tangram puzzles on a tablet. During the curious robot’s turn, it selected its estimated most learnable tangram to play, communicated its selection to the child, and then attempted at solving it. We validated the implemented architecture and showed that the robot learned, estimated its learnability, and improved when its selection was based on its learnability estimation. Moreover, we ran a comparison study between curious and non-curious robots, and showed that the robot’s curiosity-based behavior influenced the child’s selections. Based on the artificial curiosity module of the robot, we have formulated an equation that estimates each child’s moment-by-moment curiosity based on their selections. This analysis revealed an overall significant decrease in estimated curiosity during the interaction. However, this drop in estimated curiosity was significantly larger with the non-curious robot, compared to the curious one. These results suggest that the new architecture is a promising new approach to integrate state-of-the-art curiosity-based algorithms to the growing field of social robots.

Seeing, imagined, and lived

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Anandi Silva Knuppel
Keyword(s):  
Sensory Experience ◽  
Religious Practices ◽  
Daily Lives ◽  
New Approach ◽  
Theories Of Practice ◽  
Sources Of Authority ◽  
Definition Of

Scholarship on Hindu traditions and practices proposes the practice of darshan as fundamental to Hindu traditions, particularly in temple worship, observing that devotees seek out images of deities primarily to see them and “receive” their darshan. These works typically gloss the definition of darshan with a sentence or two about seeing, exchanging glances, and/or receiving blessings. In this paper, I focus on the ways in which darshan is ideally imagined in conjunction with other bodily sensory practices through sources of authority, such as texts and senior devotees, to create a specific sensory experience and expectation in the transnational Gaudiya Vaishnava community. I then look to the lived realitiesof darshan in this tradition, specifically how devotees negotiate the structures created through sources of authority in their daily lives. Through this juxtaposition of idealized and lived darshan, I argue that we need a new approach towards theories of practice to take into account the complexities of darshanic moments in this and other religious practices.

A new approach based on graph matching and evolutionary approach for sport scheduling problem

2020 ◽  
pp. 1-16
Author(s):  
Meriem Khelifa ◽  
Dalila Boughaci ◽  
Esma Aïmeur
Keyword(s):  
Graph Matching ◽  
State Of The Art ◽  
Travel Cost ◽  
Round Robin ◽  
New Approach ◽  
Traveling Tournament Problem ◽  
Significant Interest ◽  
National League ◽  
Better Than

The Traveling Tournament Problem (TTP) is concerned with finding a double round-robin tournament schedule that minimizes the total distances traveled by the teams. It has attracted significant interest recently since a favorable TTP schedule can result in significant savings for the league. This paper proposes an original evolutionary algorithm for TTP. We first propose a quick and effective constructive algorithm to construct a Double Round Robin Tournament (DRRT) schedule with low travel cost. We then describe an enhanced genetic algorithm with a new crossover operator to improve the travel cost of the generated schedules. A new heuristic for ordering efficiently the scheduled rounds is also proposed. The latter leads to significant enhancement in the quality of the schedules. The overall method is evaluated on publicly available standard benchmarks and compared with other techniques for TTP and UTTP (Unconstrained Traveling Tournament Problem). The computational experiment shows that the proposed approach could build very good solutions comparable to other state-of-the-art approaches or better than the current best solutions on UTTP. Further, our method provides new valuable solutions to some unsolved UTTP instances and outperforms prior methods for all US National League (NL) instances.

scholarly journals Bin2vec: learning representations of binary executable programs for security tasks

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Shushan Arakelyan ◽  
Sima Arasteh ◽  
Christophe Hauser ◽  
Erik Kline ◽  
Aram Galstyan
Keyword(s):  
Program Analysis ◽  
State Of The Art ◽  
Classification Error ◽  
New Approach ◽  
Convolutional Networks ◽  
Computational Program ◽  
Functional Algorithm ◽  
Binary Program ◽  
Vulnerability Discovery ◽  
Executable Programs

AbstractTackling binary program analysis problems has traditionally implied manually defining rules and heuristics, a tedious and time consuming task for human analysts. In order to improve automation and scalability, we propose an alternative direction based on distributed representations of binary programs with applicability to a number of downstream tasks. We introduce Bin2vec, a new approach leveraging Graph Convolutional Networks (GCN) along with computational program graphs in order to learn a high dimensional representation of binary executable programs. We demonstrate the versatility of this approach by using our representations to solve two semantically different binary analysis tasks – functional algorithm classification and vulnerability discovery. We compare the proposed approach to our own strong baseline as well as published results, and demonstrate improvement over state-of-the-art methods for both tasks. We evaluated Bin2vec on 49191 binaries for the functional algorithm classification task, and on 30 different CWE-IDs including at least 100 CVE entries each for the vulnerability discovery task. We set a new state-of-the-art result by reducing the classification error by 40% compared to the source-code based inst2vec approach, while working on binary code. For almost every vulnerability class in our dataset, our prediction accuracy is over 80% (and over 90% in multiple classes).

scholarly journals EfficientPS: Efficient Panoptic Segmentation

2021 ◽  
Author(s):  
Rohit Mohan ◽  
Abhinav Valada
Keyword(s):  
State Of The Art ◽  
Autonomous Robot ◽  
Multi Scale ◽  
Contextual Features ◽  
New Variant ◽  
Competent Functioning ◽  
Segmentation Task

AbstractUnderstanding the scene in which an autonomous robot operates is critical for its competent functioning. Such scene comprehension necessitates recognizing instances of traffic participants along with general scene semantics which can be effectively addressed by the panoptic segmentation task. In this paper, we introduce the Efficient Panoptic Segmentation (EfficientPS) architecture that consists of a shared backbone which efficiently encodes and fuses semantically rich multi-scale features. We incorporate a new semantic head that aggregates fine and contextual features coherently and a new variant of Mask R-CNN as the instance head. We also propose a novel panoptic fusion module that congruously integrates the output logits from both the heads of our EfficientPS architecture to yield the final panoptic segmentation output. Additionally, we introduce the KITTI panoptic segmentation dataset that contains panoptic annotations for the popularly challenging KITTI benchmark. Extensive evaluations on Cityscapes, KITTI, Mapillary Vistas and Indian Driving Dataset demonstrate that our proposed architecture consistently sets the new state-of-the-art on all these four benchmarks while being the most efficient and fast panoptic segmentation architecture to date.

scholarly journals Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 230 ◽  
Author(s):  
Slavisa Tomic ◽  
Marko Beko
Keyword(s):  
State Of The Art ◽  
Hybrid Approach ◽  
Critical Distance ◽  
Heuristic Approach ◽  
Target Localization ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Individual Measurement ◽  
New Approach ◽  
Non Line Of Sight

This work addresses the problem of target localization in adverse non-line-of-sight (NLOS) environments by using received signal strength (RSS) and time of arrival (TOA) measurements. It is inspired by a recently published work in which authors discuss about a critical distance below and above which employing combined RSS-TOA measurements is inferior to employing RSS-only and TOA-only measurements, respectively. Here, we revise state-of-the-art estimators for the considered target localization problem and study their performance against their counterparts that employ each individual measurement exclusively. It is shown that the hybrid approach is not the best one by default. Thus, we propose a simple heuristic approach to choose the best measurement for each link, and we show that it can enhance the performance of an estimator. The new approach implicitly relies on the concept of the critical distance, but does not assume certain link parameters as given. Our simulations corroborate with findings available in the literature for line-of-sight (LOS) to a certain extent, but they indicate that more work is required for NLOS environments. Moreover, they show that the heuristic approach works well, matching or even improving the performance of the best fixed choice in all considered scenarios.

scholarly journals Ambient Intelligence Based on IoT for Assisting People with Alzheimer’s Disease Through Context Histories

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1260
Author(s):  
Savanna Denega Machado ◽  
João Elison da Rosa Tavares ◽  
Márcio Garcia Martins ◽  
Jorge Luis Victória Barbosa ◽  
Gabriel Villarrubia González ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
State Of The Art ◽  
Physiological Data ◽  
Data Sets ◽  
Scientific Contribution ◽  
Daily Lives ◽  
Iot Applications ◽  
Methodological Aspects ◽  
Dangerous Behaviors

New Internet of Things (IoT) applications are enabling the development of projects that help with monitoring people with different diseases in their daily lives. Alzheimer’s is a disease that affects neurological functions and needs support to maintain maximum independence and security of patients during this stage of life, as the cure and reversal of symptoms have not yet been discovered. The IoT-based monitoring system provides the caregivers’ support in monitoring people with Alzheimer’s disease (AD). This paper presents an ontology-based computational model that receives physiological data from external IoT applications, allowing identification of potentially dangerous behaviors for patients with AD. The main scientific contribution of this work is the specification of a model focusing on Alzheimer’s disease using the analysis of context histories and context prediction, which, considering the state of the art, is the only one that uses analysis of context histories to perform predictions. In this research, we also propose a simulator to generate activities of the daily life of patients, allowing the creation of data sets. These data sets were used to evaluate the contributions of the model and were generated according to the standardization of the ontology. The simulator generated 1026 scenarios applied to guide the predictions, which achieved average accurary of 97.44%. The experiments also allowed the learning of 20 relevant lessons on technological, medical, and methodological aspects that are recorded in this article.

Social Robots in Postural Education: A New Approach to Address Body Consciousness in ASD Children

2014 ◽  
pp. 290-299 ◽  
Author(s):  
Giuseppe Palestra ◽  
Ilaria Bortone ◽  
Dario Cazzato ◽  
Francesco Adamo ◽  
Alberto Argentiero ◽  
...  
Keyword(s):  
Social Robots ◽  
Body Consciousness ◽  
New Approach

A New Approach for Autonomous Robot Obstacle Avoidance Using PSD Infrared Sensor Combined with Digital Compass

2014 ◽  
Vol 511-512 ◽  
pp. 101-104 ◽  
Author(s):  
Yang Xue ◽  
Jun Tao Yang ◽  
Ya Ling Dong ◽  
Jia Li Shen ◽  
Ru Peng ◽  
...  
Keyword(s):  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Autonomous Robot ◽  
Experimental Results ◽  
Position Sensitive Detector ◽  
Infrared Sensor ◽  
Sensitive Detector ◽  
New Approach ◽  
Position Sensitive

This paper presents a new approach for obstacle avoidance of small mobile robots, which combine the position sensitive detector (PSD) with digital compass. It is important for an autonomous robot to explore its surroundings in performing the task of localization and navigation for searching. Because of the complexity of the environment, one simple kind of sensors is not sufficient for robot to accomplish these tasks. In this paper, the small mobile robots are enabled to identify barriers and distinguish surroundings by using the angle signal from the digital compass which is generally mounted on the robot. Experimental results indicate that this approach based on digital compass shows great potential in autonomous robot obstacle avoidance.

A Cognitive Architecture for Social Robots

2018 ◽  
Author(s):  
Ignazio Infantino ◽  
Agnese Augello ◽  
Umberto Maniscalto ◽  
Giovanni Pilato ◽  
Filippo Vella
Keyword(s):  
Cognitive Architecture ◽  
Social Robots

scholarly journals TripletProt: Deep Representation Learning of Proteins based on Siamese Networks

2020 ◽  
Author(s):  
Esmaeil Nourani ◽  
Ehsaneddin Asgari ◽  
Alice C. McHardy ◽  
Mohammad R.K. Mofrad
Keyword(s):  
Functional Annotation ◽  
Cellular Localization ◽  
State Of The Art ◽  
Language Model ◽  
Representation Learning ◽  
Learning Problems ◽  
Ppi Network ◽  
New Approach ◽  
Protein Protein Interaction ◽  
Siamese Networks

AbstractWe introduce TripletProt, a new approach for protein representation learning based on the Siamese neural networks. We evaluate TripletProt comprehensively in protein functional annotation tasks including sub-cellular localization (14 categories) and gene ontology prediction (more than 2000 classes), which are both challenging multi-class multi-label classification machine learning problems. We compare the performance of TripletProt with the state-of-the-art approaches including recurrent language model-based approach (i.e., UniRep), as well as protein-protein interaction (PPI) network and sequence-based method (i.e., DeepGO). Our TripletProt showed an overall improvement of F1 score in the above mentioned comprehensive functional annotation tasks, solely relying on the PPI network. TripletProt and in general Siamese Network offer great potentials for the protein informatics tasks and can be widely applied to similar tasks.

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