3,530 research outputs found
Prediction Markets: A Systematic Review and Meta-Analysis
Prediction markets (PM) have drawn considerable attention in recent years as a tool for forecasting events. Studies surveying and examining relevant the trends of PM using traditional approaches have been reported in the literature. However, research using meta-analysis to review Prediction markets systems is very limited in Management Information System (MIS). This paper aimed to fill this gap by using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to study Prediction markets trends over the past decades. Our results are as follows. First, we find that shows that more than 64% of academic studies on Prediction markets are published in top journals such as Journal of the Association for Information Systems, Journal of Consumer Research and Information Systems Research. Second, we showed that Prediction markets applications can be can be divided into two groups: internal use PMS and general public usage. Finally, our significant meta-analysis result show that on average prediction markets is 79% more accurate than alternative forecast methods based
Maat: Performance Metric Anomaly Anticipation for Cloud Services with Conditional Diffusion
Ensuring the reliability and user satisfaction of cloud services necessitates
prompt anomaly detection followed by diagnosis.
Existing techniques for anomaly detection focus solely on real-time
detection, meaning that anomaly alerts are issued as soon as anomalies occur.
However, anomalies can propagate and escalate into failures, making
faster-than-real-time anomaly detection highly desirable for expediting
downstream analysis and intervention.
This paper proposes Maat, the first work to address anomaly anticipation of
performance metrics in cloud services.
Maat adopts a novel two-stage paradigm for anomaly anticipation, consisting
of metric forecasting and anomaly detection on forecasts.
The metric forecasting stage employs a conditional denoising diffusion model
to enable multi-step forecasting in an auto-regressive manner.
The detection stage extracts anomaly-indicating features based on domain
knowledge and applies isolation forest with incremental learning to detect
upcoming anomalies.
Thus, our method can uncover anomalies that better conform to human
expertise.
Evaluation on three publicly available datasets demonstrates that Maat can
anticipate anomalies faster than real-time comparatively or more effectively
compared with state-of-the-art real-time anomaly detectors.
We also present cases highlighting Maat's success in forecasting abnormal
metrics and discovering anomalies.Comment: This paper has been accepted by the Research track of the 38th
IEEE/ACM International Conference on Automated Software Engineering (ASE
2023
Mapping (Dis-)Information Flow about the MH17 Plane Crash
Digital media enables not only fast sharing of information, but also
disinformation. One prominent case of an event leading to circulation of
disinformation on social media is the MH17 plane crash. Studies analysing the
spread of information about this event on Twitter have focused on small,
manually annotated datasets, or used proxys for data annotation. In this work,
we examine to what extent text classifiers can be used to label data for
subsequent content analysis, in particular we focus on predicting pro-Russian
and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though
we find that a neural classifier improves over a hashtag based baseline,
labeling pro-Russian and pro-Ukrainian content with high precision remains a
challenging problem. We provide an error analysis underlining the difficulty of
the task and identify factors that might help improve classification in future
work. Finally, we show how the classifier can facilitate the annotation task
for human annotators
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
Entities with quantities : extraction, search, and ranking
Quantities are more than numeric values. They denote measures of the world’s entities such as heights of buildings, running times of athletes, energy efficiency of car models or energy production of power plants, all expressed in numbers with associated units. Entity-centric search and question answering (QA) are well supported by modern search engines. However, they do not work well when the queries involve quantity filters, such as searching for athletes who ran 200m under 20 seconds or companies with quarterly revenue above $2 Billion. State-of-the-art systems fail to understand the quantities, including the condition (less than, above, etc.), the unit of interest (seconds, dollar, etc.), and the context of the quantity (200m race, quarterly revenue, etc.). QA systems based on structured knowledge bases (KBs) also fail as quantities are poorly covered by state-of-the-art KBs. In this dissertation, we developed new methods to advance the state-of-the-art on quantity knowledge extraction and search.Zahlen sind mehr als nur numerische Werte. Sie beschreiben Maße von Entitäten wie die Höhe von Gebäuden, die Laufzeit von Sportlern, die Energieeffizienz von Automodellen oder die Energieerzeugung von Kraftwerken - jeweils ausgedrückt durch Zahlen mit zugehörigen Einheiten. Entitätszentriete Anfragen und direktes Question-Answering werden von Suchmaschinen häufig gut unterstützt. Sie funktionieren jedoch nicht gut, wenn die Fragen Zahlenfilter beinhalten, wie z. B. die Suche nach Sportlern, die 200m unter 20 Sekunden gelaufen sind, oder nach Unternehmen mit einem Quartalsumsatz von über 2 Milliarden US-Dollar. Selbst moderne Systeme schaffen es nicht, Quantitäten, einschließlich der genannten Bedingungen (weniger als, über, etc.), der Maßeinheiten (Sekunden, Dollar, etc.) und des Kontexts (200-Meter-Rennen, Quartalsumsatz usw.), zu verstehen. Auch QA-Systeme, die auf strukturierten Wissensbanken (“Knowledge Bases”, KBs) aufgebaut sind, versagen, da quantitative Eigenschaften von modernen KBs kaum erfasst werden. In dieser Dissertation werden neue Methoden entwickelt, um den Stand der Technik zur Wissensextraktion und -suche von Quantitäten voranzutreiben. Unsere Hauptbeiträge sind die folgenden: • Zunächst präsentieren wir Qsearch [Ho et al., 2019, Ho et al., 2020] – ein System, das mit erweiterten Fragen mit Quantitätsfiltern umgehen kann, indem es Hinweise verwendet, die sowohl in der Frage als auch in den Textquellen vorhanden sind. Qsearch umfasst zwei Hauptbeiträge. Der erste Beitrag ist ein tiefes neuronales Netzwerkmodell, das für die Extraktion quantitätszentrierter Tupel aus Textquellen entwickelt wurde. Der zweite Beitrag ist ein neuartiges Query-Matching-Modell zum Finden und zur Reihung passender Tupel. • Zweitens, um beim Vorgang heterogene Tabellen einzubinden, stellen wir QuTE [Ho et al., 2021a, Ho et al., 2021b] vor – ein System zum Extrahieren von Quantitätsinformationen aus Webquellen, insbesondere Ad-hoc Webtabellen in HTML-Seiten. Der Beitrag von QuTE umfasst eine Methode zur Verknüpfung von Quantitäts- und Entitätsspalten, für die externe Textquellen genutzt werden. Zur Beantwortung von Fragen kontextualisieren wir die extrahierten Entitäts-Quantitäts-Paare mit informativen Hinweisen aus der Tabelle und stellen eine neue Methode zur Konsolidierung und verbesserteer Reihung von Antwortkandidaten durch Inter-Fakten-Konsistenz vor. • Drittens stellen wir QL [Ho et al., 2022] vor – eine Recall-orientierte Methode zur Anreicherung von Knowledge Bases (KBs) mit quantitativen Fakten. Moderne KBs wie Wikidata oder YAGO decken viele Entitäten und ihre relevanten Informationen ab, übersehen aber oft wichtige quantitative Eigenschaften. QL ist frage-gesteuert und basiert auf iterativem Lernen mit zwei Hauptbeiträgen, um die KB-Abdeckung zu verbessern. Der erste Beitrag ist eine Methode zur Expansion von Fragen, um einen größeren Pool an Faktenkandidaten zu erfassen. Der zweite Beitrag ist eine Technik zur Selbstkonsistenz durch Berücksichtigung der Werteverteilungen von Quantitäten
A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization
Influence Maximization (IM) is a classical combinatorial optimization
problem, which can be widely used in mobile networks, social computing, and
recommendation systems. It aims at selecting a small number of users such that
maximizing the influence spread across the online social network. Because of
its potential commercial and academic value, there are a lot of researchers
focusing on studying the IM problem from different perspectives. The main
challenge comes from the NP-hardness of the IM problem and \#P-hardness of
estimating the influence spread, thus traditional algorithms for overcoming
them can be categorized into two classes: heuristic algorithms and
approximation algorithms. However, there is no theoretical guarantee for
heuristic algorithms, and the theoretical design is close to the limit.
Therefore, it is almost impossible to further optimize and improve their
performance. With the rapid development of artificial intelligence, the
technology based on Machine Learning (ML) has achieved remarkable achievements
in many fields. In view of this, in recent years, a number of new methods have
emerged to solve combinatorial optimization problems by using ML-based
techniques. These methods have the advantages of fast solving speed and strong
generalization ability to unknown graphs, which provide a brand-new direction
for solving combinatorial optimization problems. Therefore, we abandon the
traditional algorithms based on iterative search and review the recent
development of ML-based methods, especially Deep Reinforcement Learning, to
solve the IM problem and other variants in social networks. We focus on
summarizing the relevant background knowledge, basic principles, common
methods, and applied research. Finally, the challenges that need to be solved
urgently in future IM research are pointed out.Comment: 45 page
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