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Sequence Adaptation via Reinforcement Learning in Recommender Systems [article]

Stefanos Antaris, Dimitrios Rafailidis
<span title="2021-07-31">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Accounting for the fact that users have different sequential patterns, the main drawback of state-of-the-art recommendation strategies is that a fixed sequence length of user-item interactions is required as input to train the models. This might limit the recommendation accuracy, as in practice users follow different trends on the sequential recommendations. Hence, baseline strategies might ignore important sequential interactions or add noise to the models with redundant interactions,
more &raquo; ... on the variety of users' sequential behaviours. To overcome this problem, in this study we propose the SAR model, which not only learns the sequential patterns but also adjusts the sequence length of user-item interactions in a personalized manner. We first design an actor-critic framework, where the RL agent tries to compute the optimal sequence length as an action, given the user's state representation at a certain time step. In addition, we optimize a joint loss function to align the accuracy of the sequential recommendations with the expected cumulative rewards of the critic network, while at the same time we adapt the sequence length with the actor network in a personalized manner. Our experimental evaluation on four real-world datasets demonstrates the superiority of our proposed model over several baseline approaches. Finally, we make our implementation publicly available at https://github.com/stefanosantaris/sar.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.01442v1">arXiv:2108.01442v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/m6f7wm5pzrgkvoumwjpv2b6ls4">fatcat:m6f7wm5pzrgkvoumwjpv2b6ls4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210805051204/https://arxiv.org/pdf/2108.01442v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/53/ba/53ba9a38e3f17c1c22c5cf2fc9e05104ba572ecc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.01442v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

On Estimating the Training Cost of Conversational Recommendation Systems [article]

Stefanos Antaris, Dimitrios Rafailidis, Mohammad Aliannejadi
<span title="2020-11-10">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Conversational recommendation systems have recently gain a lot of attention, as users can continuously interact with the system over multiple conversational turns. However, conversational recommendation systems are based on complex neural architectures, thus the training cost of such models is high. To shed light on the high computational training time of state-of-the art conversational models, we examine five representative strategies and demonstrate this issue. Furthermore, we discuss
more &raquo; ... ways to cope with the high training cost following knowledge distillation strategies, where we detail the key challenges to reduce the online inference time of the high number of model parameters in conversational recommendation systems
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.05302v1">arXiv:2011.05302v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kr7ua6ipn5fhrgkqdydwe426oe">fatcat:kr7ua6ipn5fhrgkqdydwe426oe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201112025344/https://arxiv.org/pdf/2011.05302v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f2/4b/f24b3784ad95fb39935f7e6d7ba086693bbad9b4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.05302v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Meta-Reinforcement Learning via Buffering Graph Signatures for Live Video Streaming Events [article]

Stefanos Antaris, Dimitrios Rafailidis, Sarunas Girdzijauskas
<span title="2021-10-03">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this study, we present a meta-learning model to adapt the predictions of the network's capacity between viewers who participate in a live video streaming event. We propose the MELANIE model, where an event is formulated as a Markov Decision Process, performing meta-learning on reinforcement learning tasks. By considering a new event as a task, we design an actor-critic learning scheme to compute the optimal policy on estimating the viewers' high-bandwidth connections. To ensure fast
more &raquo; ... n to new connections or changes among viewers during an event, we implement a prioritized replay memory buffer based on the Kullback-Leibler divergence of the reward/throughput of the viewers' connections. Moreover, we adopt a model-agnostic meta-learning framework to generate a global model from past events. As viewers scarcely participate in several events, the challenge resides on how to account for the low structural similarity of different events. To combat this issue, we design a graph signature buffer to calculate the structural similarities of several streaming events and adjust the training of the global model accordingly. We evaluate the proposed model on the link weight prediction task on three real-world datasets of live video streaming events. Our experiments demonstrate the effectiveness of our proposed model, with an average relative gain of 25% against state-of-the-art strategies. For reproduction purposes, our evaluation datasets and implementation are publicly available at https://github.com/stefanosantaris/melanie
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.09412v1">arXiv:2111.09412v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3mml2xxlrrconkowa4kkvncoee">fatcat:3mml2xxlrrconkowa4kkvncoee</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211122181611/https://arxiv.org/pdf/2111.09412v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/67/da/67da847f46f1ba56d6e7ffd2eefc8e73a439b6a9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.09412v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

VStreamDRLS: Dynamic Graph Representation Learning with Self-Attention for Enterprise Distributed Video Streaming Solutions [article]

Stefanos Antaris, Dimitrios Rafailidis
<span title="2020-11-11">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Live video streaming has become a mainstay as a standard communication solution for several enterprises worldwide. To efficiently stream high-quality live video content to a large amount of offices, companies employ distributed video streaming solutions which rely on prior knowledge of the underlying evolving enterprise network. However, such networks are highly complex and dynamic. Hence, to optimally coordinate the live video distribution, the available network capacity between viewers has to
more &raquo; ... be accurately predicted. In this paper we propose a graph representation learning technique on weighted and dynamic graphs to predict the network capacity, that is the weights of connections/links between viewers/nodes. We propose VStreamDRLS, a graph neural network architecture with a self-attention mechanism to capture the evolution of the graph structure of live video streaming events. VStreamDRLS employs the graph convolutional network (GCN) model over the duration of a live video streaming event and introduces a self-attention mechanism to evolve the GCN parameters. In doing so, our model focuses on the GCN weights that are relevant to the evolution of the graph and generate the node representation, accordingly. We evaluate our proposed approach on the link prediction task on two real-world datasets, generated by enterprise live video streaming events. The duration of each event lasted an hour. The experimental results demonstrate the effectiveness of VStreamDRLS when compared with state-of-the-art strategies. Our evaluation datasets and implementation are publicly available at https://github.com/stefanosantaris/vstreamdrls
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.05671v1">arXiv:2011.05671v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/a2azk3pgzng25b5xczkszikda4">fatcat:a2azk3pgzng25b5xczkszikda4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201113043045/https://arxiv.org/pdf/2011.05671v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/6b/65/6b6508c7144bb61b2be6e8289c81639bedf3a98f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.05671v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Multi-Task Learning for User Engagement and Adoption in Live Video Streaming Events [article]

Stefanos Antaris and Dimitrios Rafailidis and Romina Arriaza
<span title="2021-06-18">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Nowadays, live video streaming events have become a mainstay in viewer's communication in large international enterprises. Provided that viewers are distributed worldwide, the main challenge resides on how to schedule the optimal event's time so as to improve both the viewer's engagement and adoption. In this paper we present a multi-task deep reinforcement learning model to select the time of a live video streaming event, aiming to optimize the viewer's engagement and adoption at the same
more &raquo; ... We consider the engagement and adoption of the viewers as independent tasks and formulate a unified loss function to learn a common policy. In addition, we account for the fact that each task might have different contribution to the training strategy of the agent. Therefore, to determine the contribution of each task to the agent's training, we design a Transformer's architecture for the state-action transitions of each task. We evaluate our proposed model on four real-world datasets, generated by the live video streaming events of four large enterprises spanning from January 2019 until March 2021. Our experiments demonstrate the effectiveness of the proposed model when compared with several state-of-the-art strategies. For reproduction purposes, our evaluation datasets and implementation are publicly available at https://github.com/stefanosantaris/merlin.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.10305v1">arXiv:2106.10305v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/colemm4i7ngxrkljvi32333q5e">fatcat:colemm4i7ngxrkljvi32333q5e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210623070305/https://arxiv.org/pdf/2106.10305v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8d/a2/8da29d273f9e9d605d3c68d21e03ebb42846d116.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.10305v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Deep Graph Reinforcement Learning Model for Improving User Experience in Live Video Streaming [article]

Stefanos Antaris, Dimitrios Rafailidis, Sarunas Girdzijauskas
<span title="2021-07-28">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper we present a deep graph reinforcement learning model to predict and improve the user experience during a live video streaming event, orchestrated by an agent/tracker. We first formulate the user experience prediction problem as a classification task, accounting for the fact that most of the viewers at the beginning of an event have poor quality of experience due to low-bandwidth connections and limited interactions with the tracker. In our model we consider different factors that
more &raquo; ... nfluence the quality of user experience and train the proposed model on diverse state-action transitions when viewers interact with the tracker. In addition, provided that past events have various user experience characteristics we follow a gradient boosting strategy to compute a global model that learns from different events. Our experiments with three real-world datasets of live video streaming events demonstrate the superiority of the proposed model against several baseline strategies. Moreover, as the majority of the viewers at the beginning of an event has poor experience, we show that our model can significantly increase the number of viewers with high quality experience by at least 75% over the first streaming minutes. Our evaluation datasets and implementation are publicly available at https://publicresearch.z13.web.core.windows.net
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.13619v1">arXiv:2107.13619v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iyi2fylrwfgcnb3ton3r7kskbe">fatcat:iyi2fylrwfgcnb3ton3r7kskbe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210804204337/https://arxiv.org/pdf/2107.13619v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4f/d8/4fd88556ebd92d20af81ef96f13c7840d731a925.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.13619v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Cloud-based architectures for geo-located blogosphere dynamics detection

Athena Vakali, Stefanos Antaris, Maria Giatsoglou
<span title="2016-11-03">2016</span> <i title="Whioce Publishing Pte Ltd"> Journal of Smart Cities </i> &nbsp;
Social networking data threads emerge rapidly and such crowd-driven big data streams are valuable for detecting trends and opinions. For such analytics, conventional data mining approaches are challenged by both high-dimensionality and scalability concerns. Here, we leverage on the Cloud4Trends framework for collecting and analyzing geo-located microblogging content, partitioned into clusters under cloud-based infrastructures. Different cloud architectures are proposed to offer flexible
more &raquo; ... s for geo-located data analytics with emphasis on incremental trend analysis. The proposed architectures are largely based on a set of service modules which facilitate the deployment of the experimentation on cloud infrastructures. Several experimentation remarks are highlighted to showcase the requirements and testing capabilities of different cloud computing settings.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18063/jsc.2016.01.006">doi:10.18063/jsc.2016.01.006</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/o6yml7nurrhoxnywnj3m37iaga">fatcat:o6yml7nurrhoxnywnj3m37iaga</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180721224237/http://ojs.udspub.com/index.php/jsc/article/download/66/62" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/19/99/19993561abd4f4552a9a63b6da2988ecf63b4d01.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18063/jsc.2016.01.006"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

EGAD: Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live Video Streaming Events [article]

Stefanos Antaris, Dimitrios Rafailidis, Sarunas Girdzijauskas
<span title="2020-11-11">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this study, we present a dynamic graph representation learning model on weighted graphs to accurately predict the network capacity of connections between viewers in a live video streaming event. We propose EGAD, a neural network architecture to capture the graph evolution by introducing a self-attention mechanism on the weights between consecutive graph convolutional networks. In addition, we account for the fact that neural architectures require a huge amount of parameters to train, thus
more &raquo; ... reasing the online inference latency and negatively influencing the user experience in a live video streaming event. To address the problem of the high online inference of a vast number of parameters, we propose a knowledge distillation strategy. In particular, we design a distillation loss function, aiming to first pretrain a teacher model on offline data, and then transfer the knowledge from the teacher to a smaller student model with less parameters. We evaluate our proposed model on the link prediction task on three real-world datasets, generated by live video streaming events. The events lasted 80 minutes and each viewer exploited the distribution solution provided by the company Hive Streaming AB. The experiments demonstrate the effectiveness of the proposed model in terms of link prediction accuracy and number of required parameters, when evaluated against state-of-the-art approaches. In addition, we study the distillation performance of the proposed model in terms of compression ratio for different distillation strategies, where we show that the proposed model can achieve a compression ratio up to 15:100, preserving high link prediction accuracy. For reproduction purposes, our evaluation datasets and implementation are publicly available at https://stefanosantaris.github.io/EGAD.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.05705v1">arXiv:2011.05705v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kdx4qoupmjcjfi6owyrytxjs4e">fatcat:kdx4qoupmjcjfi6owyrytxjs4e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201113025323/https://arxiv.org/pdf/2011.05705v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/1c/00/1c003dd4b044aac639d3a9b283c92d0dea21d5bb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.05705v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Link injection for boosting information spread in social networks

Stefanos Antaris, Dimitrios Rafailidis, Alexandros Nanopoulos
<span title="2014-11-15">2014</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3xvqvdkqejfvdeozx2l3c3rxti" style="color: black;">Social Network Analysis and Mining</a> </i> &nbsp;
Social media have become popular platforms for spreading information. Several applications, such as 'viral marketing', pause the requirement for attaining large-scale information spread in the form of word-ofmouth that reaches a large number of users. In this paper, we propose a novel method that predicts new social links that can be inserted among existing users of a social network, aiming directly at boosting information spread and increasing its reach. We refer to this task as 'link
more &raquo; ... ', because unlike most existing people-recommendation methods, it focuses directly on information spread. A set of candidate links for injection is first predicted in a collaborative-filtering fashion, which generates personalized candidate connections. We select among the candidate links a constrained number that will be finally injected based on a novel application of a score that measures the importance of nodes in a social graph, following the strategy of injecting links adjacent to the most important nodes. The proposed method is suitable for real-world applications, because the injected links manage to substantially increase the reach of information spread by controlling at the same time the number of injected links not to affect the user experience. We evaluate the performance of our proposed methodology by examining several real data sets from social networks under several distinct factors. The experimentation demonstrates the effectiveness of our proposed method, which increases the spread by more than a twofold factor by injecting as few as half of the existing number of links.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s13278-014-0236-y">doi:10.1007/s13278-014-0236-y</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rmrxarn54zfv5h32xjllb5rovq">fatcat:rmrxarn54zfv5h32xjllb5rovq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190430165319/http://kth.diva-portal.org/smash/get/diva2:1297686/FULLTEXT01" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3a/f6/3af675c1552684d3b089139ddf52f2f0b328aea0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s13278-014-0236-y"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

A Free Online Parallel Corpus Construction Tool for Language Teachers and Learners

Fryni Kakoyianni-Doa, Stefanos Antaris, Eleni Tziafa
<span title="">2013</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/trfcnrckdvgftcxiz3i6lginty" style="color: black;">Procedia - Social and Behavioral Sciences</a> </i> &nbsp;
In this article we present a free online parallel corpus construction tool, the PENCIL tool (Pedagogy Enhancement through Corpora in Language learning), part of the SOURCe Project, a French-Greek parallel corpora collection developed for the University of Cyprus. The goal of this project is to provide an assistive and handy tool for teachers and learners f f , in order to be used as an Open Educational Resource. This tool enables users, who might not have the required programming skills, to
more &raquo; ... te and customize web-based corpora, by uploading and aligning their own texts.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.sbspro.2013.10.679">doi:10.1016/j.sbspro.2013.10.679</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zxeqydyz6faw3mwuruq5bxqcye">fatcat:zxeqydyz6faw3mwuruq5bxqcye</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190416124610/https://core.ac.uk/download/pdf/82770794.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d3/2a/d32a6edba801afb6f441d29e30359a8447ea57b2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.sbspro.2013.10.679"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

Social networking trends and dynamics detection via a cloud-based framework design

Athena Vakali, Maria Giatsoglou, Stefanos Antaris
<span title="">2012</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s4hirppq3jalbopssw22crbwwa" style="color: black;">Proceedings of the 21st international conference companion on World Wide Web - WWW &#39;12 Companion</a> </i> &nbsp;
Social networking media generate huge content streams, which leverage, both academia and developers efforts in providing unbiased, powerful indications of users' opinion and interests. Here, we present Cloud4Trends, a framework for collecting and analyzing user generated content through microblogging and blogging applications, both separately and jointly, focused on certain geographical areas, towards the identification of the most significant topics using trend analysis techniques. The cloud
more &raquo; ... mputing paradigm appears to offer a significant benefit in order to make such applications viable considering that the massive data sizes produced daily impose the need of a scalable and powerful infrastructure. Cloud4Trends constitutes an efficient Cloud-based approach in order to solve the online trend tracking problem based on Web 2.0 sources. A detailed system architecture model is also proposed, which is largely based on a set of service modules developed within the VENUS-C research project to facilitate the deployment of research applications on Cloud infrastructures.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2187980.2188263">doi:10.1145/2187980.2188263</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/www/VakaliGA12.html">dblp:conf/www/VakaliGA12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/acvu73g2sjf4rjk4vhyf4guvde">fatcat:acvu73g2sjf4rjk4vhyf4guvde</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20120906215422/http://www2012.wwwconference.org:80/proceedings/companion/p1213.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/bf/17/bf17c24297db1468fb3f8fe922eb2e90673b4f55.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2187980.2188263"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Smart cities and cloud computing: Introduction to the special issue

Christina Kakderi, Nicos Komninos, Panagiotis Tsarchopoulos
<span title="2016-11-03">2016</span> <i title="Whioce Publishing Pte Ltd"> Journal of Smart Cities </i> &nbsp;
The paper "Cloud-based architectures for geo-located blogosphere dynamics detection", by Athena Vakali, Stefanos Antaris and Maria Giatsoglou, focuses on microblogging content under cloud-based infrastructure  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18063/jsc.2016.01.001">doi:10.18063/jsc.2016.01.001</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xhvsczoqejg4jjyl543cqj5kdy">fatcat:xhvsczoqejg4jjyl543cqj5kdy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180721194224/http://ojs.udspub.com/index.php/jsc/article/download/61/57" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e7/91/e791be50f91a20d9335d055ee45bc2057c63af95.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18063/jsc.2016.01.001"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

LiMNet: Early-Stage Detection of IoT Botnets with Lightweight Memory Networks

Lodovico Giaretta, Ahmed Lekssays, Barbara Carminati, Elena Ferrari, Sarunas Girdzijauskas
<span title="2021-09-22">2021</span> <i title="Zenodo"> Zenodo </i> &nbsp;
Acknowledgements The authors would like to thank Stefanos Antaris (affiliated with KTH and Hive Streaming AB) for the insightful discussions and literature recommendations that helped shape the direction  ... 
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<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210930034110/https://zenodo.org/record/5520868/files/IoT_Botnet_Detection_with_Lightweight_Memory_Networks.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/df/cf/dfcf58069c8f80fb92700a3048165f241f5e97e6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.5520867"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

Annexes [chapter]

<span title="2022-01-07">2022</span> <i title="Brill | Nijhoff"> Contract Interpretation in Investment Treaty Arbitration </i> &nbsp;
Marco Gavazzi and Stefano Gavazzi v. Romania, icsid Case No. arb/ 12/ 25 (Italy -Romania bit) 517. Marfin Investment Group v.  ...  Antaris Solar GmbH and Dr. Michael Göde v. Czech Republic, pca Case No. 2014-01 (Energy Charter Treaty (ect), Germany -Slovakia bit) 75.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1163/9789004414709_011">doi:10.1163/9789004414709_011</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4yiglmbxcbeunfccegsg4mytzq">fatcat:4yiglmbxcbeunfccegsg4mytzq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220121174528/https://brill.com/downloadpdf/book/9789004414709/BP000013.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/87/6b/876bcd073ada95481515103e02b7ad7edf82152b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1163/9789004414709_011"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> brill.com </button> </a>

Abstracts of Papers Presented at the 2007 Pittsburgh Conference

Peter B. Stockwell
<span title="">2007</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/talareqi3ffzxfrfyeckbket34" style="color: black;">Journal of automated methods &amp; management in chemistry (Print)</a> </i> &nbsp;
Many fuel mixtures have been prepared, 160 with FT-NIR spectra were recorded with a Nicolet Antaris spectrometer interfaced to a personal computer.  ...  NEW AUTOMATED SEPTUMLESS ON-COLUMN INJECTOR FOR CAPILLARY GC Stefano Pelagatti, Andrea Cadoppi, Daniela Cavagnino, and Paolo Magni Thermo Electron Corporation, Strada Rivoltana, Rodano 20090, Italy  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2007/71943">doi:10.1155/2007/71943</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/18528514">pmid:18528514</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC2391256/">pmcid:PMC2391256</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/x3ojznxiufafpcjumeymzd5ise">fatcat:x3ojznxiufafpcjumeymzd5ise</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190303040319/http://pdfs.semanticscholar.org/c21d/4c6a020f9281be87f363a7990e8cd4cd3046.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c2/1d/c21d4c6a020f9281be87f363a7990e8cd4cd3046.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2007/71943"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2391256" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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