66 research outputs found
A Transformer-based Framework for POI-level Social Post Geolocation
POI-level geo-information of social posts is critical to many location-based
applications and services. However, the multi-modality, complexity and diverse
nature of social media data and their platforms limit the performance of
inferring such fine-grained locations and their subsequent applications. To
address this issue, we present a transformer-based general framework, which
builds upon pre-trained language models and considers non-textual data, for
social post geolocation at the POI level. To this end, inputs are categorized
to handle different social data, and an optimal combination strategy is
provided for feature representations. Moreover, a uniform representation of
hierarchy is proposed to learn temporal information, and a concatenated version
of encodings is employed to capture feature-wise positions better. Experimental
results on various social datasets demonstrate that three variants of our
proposed framework outperform multiple state-of-art baselines by a large margin
in terms of accuracy and distance error metrics.Comment: Full papers are 12 pages in length plus additional 4 pages for
references (turns to 18 pages in total after submitting to arxiv). One figure
and 5 tables are contained. This paper was submitted to ECIR 2023 for revie
Factors Impacting the Quality of User Answers on Smartphones
So far, most research investigating the predictability of human behavior,
such as mobility and social interactions, has focused mainly on the
exploitation of sensor data. However, sensor data can be difficult to capture
the subjective motivations behind the individuals' behavior. Understanding
personal context (e.g., where one is and what they are doing) can greatly
increase predictability. The main limitation is that human input is often
missing or inaccurate. The goal of this paper is to identify factors that
influence the quality of responses when users are asked about their current
context. We find that two key factors influence the quality of responses: user
reaction time and completion time. These factors correlate with various
exogenous causes (e.g., situational context, time of day) and endogenous causes
(e.g., procrastination attitude, mood). In turn, we study how these two factors
impact the quality of responses.Comment: 5 pages, 1 tabl
Editorial: Review and trend analysis of knowledge management and e-learning research
In recent years, technology-enhanced knowledge management and learning have attracted much attention from educators and researchers. Various successful applications as well as the potential of knowledge management and e-learning have been reported. In the meantime, the fast development of technologies is affecting the way of knowledge management and learning design as well as the learning context. In this special issue, 8 papers are included to address the trends of knowledge management and e-learning and to review their impacts from different perspectives. The findings reported by these papers provided valuable references for those who intend to implement technology-enhanced learning in school settings and to conduct e-learning research from innovative perspectives
Intelligent Malware Detection Using File-to-file Relations and Enhancing its Security against Adversarial Attacks
With computing devices and the Internet being indispensable in people\u27s everyday life, malware has posed serious threats to their security, making its detection of utmost concern. To protect legitimate users from the evolving malware attacks, machine learning-based systems have been successfully deployed and offer unparalleled flexibility in automatic malware detection. In most of these systems, resting on the analysis of different content-based features either statically or dynamically extracted from the file samples, various kinds of classifiers are constructed to detect malware. However, besides content-based features, file-to-file relations, such as file co-existence, can provide valuable information in malware detection and make evasion harder. To better understand the properties of file-to-file relations, we construct the file co-existence graph. Resting on the constructed graph, we investigate the semantic relatedness among files, and leverage graph inference, active learning and graph representation learning for malware detection. Comprehensive experimental results on the real sample collections from Comodo Cloud Security Center demonstrate the effectiveness of our proposed learning paradigms.
As machine learning-based detection systems become more widely deployed, the incentive for defeating them increases. Therefore, we go further insight into the arms race between adversarial malware attack and defense, and aim to enhance the security of machine learning-based malware detection systems. In particular, we first explore the adversarial attacks under different scenarios (i.e., different levels of knowledge the attackers might have about the targeted learning system), and define a general attack strategy to thoroughly assess the adversarial behaviors. Then, considering different skills and capabilities of the attackers, we propose the corresponding secure-learning paradigms to counter the adversarial attacks and enhance the security of the learning systems while not compromising the detection accuracy. We conduct a series of comprehensive experimental studies based on the real sample collections from Comodo Cloud Security Center and the promising results demonstrate the effectiveness of our proposed secure-learning models, which can be readily applied to other detection tasks
Time-Dependent Influence Measurement in Citation Networks
In every scientific discipline, researchers face two common dilemmas: where to find bleeding-edge papers and where to publish their own articles. We propose to answer these questions by looking at the influence between communities, e.g. conferences or journals. The influential conferences are those which papers are heavily cited by other conferences, i.e. they are visible, significant and inspiring. For the task of finding such influential places-to-publish, we introduce a Running Influence model that aims to discover pairwise influence between communities and evaluate the overall influence of each considered community. We have taken into consideration time aspects such as intensity of papers citations over time and difference of conferences starting years. The community influence analysis is tested on real-world data of Computer Science conferences
A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions
In recent decades, social network anonymization has become a crucial research
field due to its pivotal role in preserving users' privacy. However, the high
diversity of approaches introduced in relevant studies poses a challenge to
gaining a profound understanding of the field. In response to this, the current
study presents an exhaustive and well-structured bibliometric analysis of the
social network anonymization field. To begin our research, related studies from
the period of 2007-2022 were collected from the Scopus Database then
pre-processed. Following this, the VOSviewer was used to visualize the network
of authors' keywords. Subsequently, extensive statistical and network analyses
were performed to identify the most prominent keywords and trending topics.
Additionally, the application of co-word analysis through SciMAT and the
Alluvial diagram allowed us to explore the themes of social network
anonymization and scrutinize their evolution over time. These analyses
culminated in an innovative taxonomy of the existing approaches and
anticipation of potential trends in this domain. To the best of our knowledge,
this is the first bibliometric analysis in the social network anonymization
field, which offers a deeper understanding of the current state and an
insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure
Capturing stance dynamics in social media: open challenges and research directions
Social media platforms provide a goldmine for mining public opinion on issues
of wide societal interest and impact. Opinion mining is a problem that can be
operationalised by capturing and aggregating the stance of individual social
media posts as supporting, opposing or being neutral towards the issue at hand.
While most prior work in stance detection has investigated datasets that cover
short periods of time, interest in investigating longitudinal datasets has
recently increased. Evolving dynamics in linguistic and behavioural patterns
observed in new data require adapting stance detection systems to deal with the
changes. In this survey paper, we investigate the intersection between
computational linguistics and the temporal evolution of human communication in
digital media. We perform a critical review of emerging research considering
dynamics, exploring different semantic and pragmatic factors that impact
linguistic data in general, and stance in particular. We further discuss
current directions in capturing stance dynamics in social media. We discuss the
challenges encountered when dealing with stance dynamics, identify open
challenges and discuss future directions in three key dimensions: utterance,
context and influence
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