Computer Science: Recent submissions
Now showing items 161-180 of 549
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Nova: Value-based negotiation of norms
(ACM, 2021-08-01)Specifying a normative multiagent system (nMAS) is challenging, because different agents often have conflicting requirements. Whereas existing approaches can resolve clear-cut conflicts, tradeoffs might occur in practice ... -
Guest editorial special issue on continual unsupervised sensorimotor learning
(IEEE, 2021-06)The pursuit of higher levels of autonomy and versatility in robotics is arguably led by two main factors. First, as we push robots out of the labs and production lines, it becomes increasingly challenging to design for all ... -
Inferring cost functions using reward parameter search and policy gradient reinforcement learning
(IEEE, 2021)This study focuses on inferring cost functions of obtained movement data using reward parameter search and policy gradient based Reinforcement Learning (RL). The behavior data for this task is obtained through a series of ... -
Imitation and mirror systems in robots through Deep Modality Blending Networks
(Elsevier, 2022-02)Learning to interact with the environment not only empowers the agent with manipulation capability but also generates information to facilitate building of action understanding and imitation capabilities. This seems to be ... -
A computer science-oriented approach to introduce quantum computing to a new audience
(IEEE, 2022-02)Contribution: In this study, an alternative educational approach for introducing quantum computing to a wider audience is highlighted. The proposed methodology considers quantum computing as a generalized probability theory ... -
Can social agents efficiently perform in automated negotiation?
(MDPI, 2021-07)In the last few years, we witnessed a growing body of literature about automated negotiation. Mainly, negotiating agents are either purely self-driven by maximizing their utility function or by assuming a cooperative stance ... -
EXPECTATION: Personalized explainable artificial intelligence for decentralized agents with heterogeneous knowledge
(Springer, 2021)Explainable AI (XAI) has emerged in recent years as a set of techniques and methodologies to interpret and explain machine learning (ML) predictors. To date, many initiatives have been proposed. Nevertheless, current ... -
Data-driven bandwidth prediction models and automated model selection for low latency
(IEEE, 2021)Today's HTTP adaptive streaming solutions use a variety of algorithms to measure the available network bandwidth and predict its future values. Bandwidth prediction, which is already a difficult task, must be more accurate ... -
Artificial intelligence techniques for conflict resolution
(Springer, 2021-08)Conflict resolution is essential to obtain cooperation in many scenarios such as politics and business, as well as our day to day life. The importance of conflict resolution has driven research in many fields like anthropology, ... -
Trust me! I am a robot: an affective computational account of scaffolding in robot-robot interaction
(IEEE, 2021-08-08)Forming trust in a biological or artificial interaction partner that provides reliable strategies and employing the learned strategies to scaffold another agent are critical problems that are often addressed separately in ... -
Misclassification risk and uncertainty quantification in deep classifiers
(IEEE, 2021)In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a ... -
Learning system dynamics via deep recurrent and conditional neural systems
(IEEE, 2021)Although there are various mathematical methods for modeling system dynamics, more general solutions can be achieved using deep learning based on data. Alternative deep learning methods are presented in parallel with the ... -
Road to salvation: Streaming clients and content delivery networks working together
(IEEE, 2021-11-01)Streaming media has truly become one of the most popular applications on the Internet. Viewers are spoiled for choice, and content providers compete to provide the best viewer experience. Traditionally, it has been challenging ... -
Hierarchical mixtures of generators for adversarial learning
Generative adversarial networks (GANs) are deep neural networks that allow us to sample from an arbitrary probability distribution without explicitly estimating the distribution. There is a generator that takes a latent ... -
Data model extension impact analysis
(IEEE, 2021)Relational database schemas are subject to change. For instance, columns of a table can be modified, deleted or extended. These changes have an impact on the source code that utilizes the corresponding table. They also ... -
Dynamic filtering and prioritization of static code analysis alerts
(IEEE, 2021)We propose an approach for filtering and prioritizing static code analysis alerts while these alerts are being reviewed by the developer. We construct a Prolog knowledge base that captures the data flow information in the ... -
Time-correlated sparsification for communication-efficient federated learning
(IEEE, 2021)Federated learning (FL) enables multiple clients to collaboratively train a shared model, with the help of a parameter server (PS), without disclosing their local datasets. However, due to the increasing size of the trained ... -
Content-aware playback speed control for low-latency live streaming of sports
(The ACM Digital Library, 2021)There are two main factors that determine the viewer experience during the live streaming of sports content: latency and stalls. Latency should be low and stalls should not occur. Yet, these two factors work against each ... -
COSMOS on steroids: a Cheap detector for cheapfakes
(The ACM Digital Library, 2021)The growing prevalence of visual disinformation has become an important problem to solve nowadays. Cheapfake is a new term used for the altered media generated by non-AI techniques. In their recent COSMOS work, the authors ... -
Automated defect prioritization based on defects resolved at various project periods
(Elsevier, 2021-09)Defect prioritization is mainly a manual and error-prone task in the current state-of-the-practice. We evaluated the effectiveness of an automated approach that employs supervised machine learning. We used two alternative ...
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