Computer Science: Recent submissions
Now showing items 261-280 of 549
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Automatically learning usage behavior and generating event sequences for black-box testing of reactive systems
(The ACM Digital Library, 2019-06)We propose a novel technique based on recurrent artificial neural networks to generate test cases for black-box testing of reactive systems. We combine functional testing inputs that are automatically generated from a model ... -
Affordance-based altruistic robotic architecture for human–robot collaboration
(Sage, 2019-08)This article proposes a computational model for altruistic behavior, shows its implementation on a physical robot, and presents the results of human-robot interaction experiments conducted with the implemented system. ... -
Algorithm selection and combining multiple learners for residential energy prediction
(Elsevier, 2019-10)Balancing supply and demand management in energy grids requires knowing energy consumption in advance. Therefore, forecasting residential energy consumption accurately plays a key role for future energy systems. For this ... -
Bottom-up approaches to achieve Pareto optimal agreements in group decision making
(Springer Nature, 2019-11)In this article, we introduce a new paradigm to achieve Pareto optimality in group decision-making processes: bottom-up approaches to Pareto optimality. It is based on the idea that, while resolving a conflict in a group, ... -
Symbol emergence in cognitive developmental systems: A survey
(IEEE, 2019-12)Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and adaptation to our real-world environment. ... -
Evaluation of distributed machine learning algorithms for anomaly detection from large-scale system logs: a case study
(IEEE, 2018)Anomaly detection is a valuable feature for detecting and diagnosing faults in large-scale, distributed systems. These systems usually provide tens of millions of lines of logs that can be exploited for this purpose. ... -
QoE-aware bandwidth broker for HTTP adaptive streaming flows in an SDN-enabled HFC network
(IEEE, 2018-06)This paper proposes a software defined networking based bandwidth broker solution for improving viewer experience for any type of content delivered to any type of consumer device using HTTP adaptive streaming (HAS) in a ... -
Evidential deep learning to quantify classification uncertainty
(Neural Information Processing Systems Foundation, 2018)Deterministic neural nets have been shown to learn effective predictors on a wide range of machine learning problems. However, as the standard approach is to train the network to minimize a prediction loss, the resultant ... -
Multi-lingual depression-level assessment from conversational speech using acoustic and text features
(International Speech Communication Association, 2018)Depression is a common mental health problem around the world with a large burden on economies, well-being, hence productivity, of individuals. Its early diagnosis and treatment are critical to reduce the costs and even ... -
Aktör tabanlı sistemler için test kapsama kriterleri
(CEUR-WS, 2018)Aktör tabanlı sistemler, eşzamanlı çalışan ve birbirleri ile asenkron bir şekilde haberleşen aktör isimli otonom elemanlardan oluşmaktadırlar. Asenkron haberleşme sebebiyle aktörler arasında paylaşılan mesajların sıralaması ... -
A framework for adaptive delivery of omnidirectional video
(Society for Imaging Science and Technology, 2018)Omnidirectional or 360-degree videos are considered as a next step towards a truly immersive media experience. Such videos allow the user to change her/his viewing direction while consuming the video. The download-and-play ... -
Observing interoperability of IoT systems through model-based testing
(Springer Nature, 2018)Internet of Things (IoT) has drastically modified the industrial services provided through autonomous machine-to-machine interactions. Such systems comprise of devices manufactured by various suppliers. Verification is a ... -
Model-based runtime monitoring of smart city systems
(Elsevier, 2018)The pace of proliferation for smart systems in city wide applications is unmatched. The introduction of Internet of Things (IoT), an enabler of smart city phenomenon, has incubated a productive environment for such ... -
A novel intelligent approach for detecting DoS flooding attacks in software-defined networks
(Universitas Ahmad Dahlan, 2018-03)Software-Defined Networking (SDN) is an emerging networking paradigm that provides an advanced programming capability and moves the control functionality to a centralized controller. This paper proposes a two-stage novel ... -
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Want to play DASH?: a game theoretic approach for adaptive streaming over HTTP
(Association for Computing Machinery, Inc, 2018-06-12)In streaming media, it is imperative to deliver a good viewer experience to preserve customer loyalty. Prior research has shown that this is rather difficult when shared Internet resources struggle to meet the demand from ... -
Quickly starting media streams using QUIC
(Association for Computing Machinery, Inc, 2018-06-12)Originally proposed by Google, QUIC is a low-latency transport protocol currently being developed and specified in the IETF. QUIC's low-latency, improved congestion control, multiplexing features are promising and may help ... -
Interpretability of deep learning models: a survey of results
(IEEE, 2018-06-26)Deep neural networks have achieved near-human accuracy levels in various types of classification and prediction tasks including images, text, speech, and video data. However, the networks continue to be treated mostly as ... -
Automated objective and subjective evaluation of HTTP adaptive streaming systems
(IEEE, 2018-06-26)Streaming audio and video content currently accounts for the majority of the internet traffic and is typically deployed over the top of the existing infrastructure. We are facing the challenge of a plethora of media players ... -
Effective robot skill synthesis via divided control
(IEEE, 2018-07-02)Learning from demonstration is a powerful method for obtaining task skills, which aim to eliminate the need for explicit robot programming. Classically, the tasks are demonstrated to the robot by means of either recorded ...
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