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Добірка наукової літератури з теми "Emerging trend detection"

Автор: Grafiati

Опубліковано: 21 вересня 2023

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Статті в журналах з теми "Emerging trend detection"

Hsu, Ming-Hung, Yu-Hui Chang, and Hsin-Hsi Chen. "Temporal Correlation between Social Tags and Emerging Long-Term Trend Detection." Proceedings of the International AAAI Conference on Web and Social Media 4, no. 1 (2010): 255–58. http://dx.doi.org/10.1609/icwsm.v4i1.14049.

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Social annotation has become a popular manner for web users to manage and share their information and interests. While users' interests vary with time, tag correlation also changes from users' perspectives. In this work, we explore four methods for estimating temporal correlation between social tags and detect if a long-term trend emerges from the history of temporal correlation between two tags. Three types of trends are specified: steadily-shifting, stabilizing, and cyclic. To compare the results of the four estimation methods, an indirect evaluation is realized by applying detected trends to tag recommendation.

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VALENCIA, MARIA, CODRINA LAUTH, and ERNESTINA MENASALVAS. "EMERGING USER INTENTIONS: MATCHING USER QUERIES WITH TOPIC EVOLUTION IN NEWS TEXT STREAMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 17, supp01 (2009): 59–80. http://dx.doi.org/10.1142/s0218488509006030.

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Trend detection analysis from unstructured data poses a huge challenge to current advanced, web-enabled knowledge-based systems (KBS). Consolidated studies in topic and trend detection from text streams have concentrated so far mainly on identifying and visualizing dynamically evolving text patterns. From the knowledge modeling perspective identifying and defining new, relevant features that are able to synchronize the emergent user intentions to the dynamicity of the system's structure is a need. Additionally the advanced KBS have to remain highly sensitive to the content change, marked by evolution of trends in topics extracted from text streams. In this paper, we are describing a three-layered approach called the "user-system-content method" that is helping us to identify the most relevant knowledge mapping features derived from the USER, SYSTEM and CONTENT perspectives into an overall "context model", that will enable the advanced KBS to automatically streamline the query enrichment process in a much more user-centered, dynamical and flexible way. After a general introduction to our three-layered approach, we will describe into detail the necessary process steps for the implementation of our method and will present a case study for its integration on a real multimedia web-content portal using news streams as major source of unstructured information.

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Lackner, Bettina C., Andrea K. Steiner, Gabriele C. Hegerl, and Gottfried Kirchengast. "Atmospheric Climate Change Detection by Radio Occultation Data Using a Fingerprinting Method." Journal of Climate 24, no. 20 (2011): 5275–91. http://dx.doi.org/10.1175/2011jcli3966.1.

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Abstract The detection of climate change signals in rather short satellite datasets is a challenging task in climate research and requires high-quality data with good error characterization. Global Navigation Satellite System (GNSS) radio occultation (RO) provides a novel record of high-quality measurements of atmospheric parameters of the upper-troposphere–lower-stratosphere (UTLS) region. Because of characteristics such as long-term stability, self calibration, and a very good height resolution, RO data are well suited to investigate atmospheric climate change. This study describes the signals of ENSO and the quasi-biennial oscillation (QBO) in the data and investigates whether the data already show evidence of a forced climate change signal, using an optimal-fingerprint technique. RO refractivity, geopotential height, and temperature within two trend periods (1995–2010 intermittently and 2001–10 continuously) are investigated. The data show that an emerging climate change signal consistent with the projections of three global climate models from the Coupled Model Intercomparison Project cycle 3 (CMIP3) archive is detected for geopotential height of pressure levels at a 90% confidence level both for the intermittent and continuous period, for the latter so far in a broad 50°S–50°N band only. Such UTLS geopotential height changes reflect an overall tropospheric warming. 90% confidence is not achieved for the temperature record when only large-scale aspects of the pattern are resolved. When resolving smaller-scale aspects, RO temperature trends appear stronger than GCM-projected trends, the difference stemming mainly from the tropical lower stratosphere, allowing for climate change detection at a 95% confidence level. Overall, an emerging trend signal is thus detected in the RO climate record, which is expected to increase further in significance as the record grows over the coming years. Small natural changes during the period suggest that the detected change is mainly caused by anthropogenic influence on climate.

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Wu, Yuqi, Yuhan Deng, Longgang Zhang, Qidong Zhang, and Ran Bao. "Research on the Development of Unmanned Underwater System Detection Technology." Journal of Physics: Conference Series 2218, no. 1 (2022): 012079. http://dx.doi.org/10.1088/1742-6596/2218/1/012079.

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Abstract In recent years, the number of unmanned systems in the world has been increasing. The emerging underwater platforms such as UUV are large in number, small in size, low in speed and low in noise. In the future, a variety of ocean combat missions will use a large number of UUV platforms. The existing methods for detecting underwater vehicles mainly include sonar, magnetometer. Based on this, by referring to a large number of literatures, a review of the research progress of underwater exploration at home and abroad in recent years. And compared with the United States, Russia and other countries unmanned system detection technology and equipment, analysis of their applicable conditions and advantages and disadvantages. This paper summarizes the technical difficulties of underwater detection methods and proposes corresponding solutions. At the same time, we forecast the development trend of underwater detection technology in the future.

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SUCHIT K. RAI, SUNIL KUMAR, and MANOJ CHAUDHARY. "Detection of annual and seasonal temperature variability and change using non-parametric test- A case study of Bundelkhand region of central India." Journal of Agrometeorology 23, no. 4 (2021): 402–8. http://dx.doi.org/10.54386/jam.v23i4.144.

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Consequences of global warming and climate change are major threat to humans and their socio-economic activities. Agriculture of Bundelkhand region is supposed to be more vulnerable due to emerging scenario of climate change and poor socio-economic status of farming community. Many studies carried out elsewhere have shown evidence of regional temperature variability along with global climate changes. This study focuses on the temporal variability and trend in annual and seasonal temperature (1901-2012) at six locations of Bundelkhand region. The results of the analysis reveal that the annual maximum (TMax) and minimum (TMin) temperature has significantly increasing trend in all the locations in the range of 0.5 to 2.0oC 100 year-1 and 0.5 to 1.1 oC 100 year-1, respectively. Seasonal analysis revealed warming trend in both TMax (0.6-2.6oC100 year-1) and TMin (0.9 to 2.3 oC 100 year-1) during post-monsoon and winter season in all the locations. Majority of the locations showed cooling trend (0.3-1.0 oC 100 year-1), in the mean maximum and minimum temperature during monsoon season except at two locations i.e Jhansi and Banda. However, a significant positive trends (2.9 oC) in the TMin was found for the period of hundred years at Banda district during monsoon season.

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Parlina, Anne, Kalamullah Ramli, and Hendri Murfi. "Exposing Emerging Trends in Smart Sustainable City Research Using Deep Autoencoders-Based Fuzzy C-Means." Sustainability 13, no. 5 (2021): 2876. http://dx.doi.org/10.3390/su13052876.

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The literature discussing the concepts, technologies, and ICT-based urban innovation approaches of smart cities has been growing, along with initiatives from cities all over the world that are competing to improve their services and become smart and sustainable. However, current studies that provide a comprehensive understanding and reveal smart and sustainable city research trends and characteristics are still lacking. Meanwhile, policymakers and practitioners alike need to pursue progressive development. In response to this shortcoming, this research offers content analysis studies based on topic modeling approaches to capture the evolution and characteristics of topics in the scientific literature on smart and sustainable city research. More importantly, a novel topic-detecting algorithm based on the deep learning and clustering techniques, namely deep autoencoders-based fuzzy C-means (DFCM), is introduced for analyzing the research topic trend. The topics generated by this proposed algorithm have relatively higher coherence values than those generated by previously used topic detection methods, namely non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), and eigenspace-based fuzzy C-means (EFCM). The 30 main topics that appeared in topic modeling with the DFCM algorithm were classified into six groups (technology, energy, environment, transportation, e-governance, and human capital and welfare) that characterize the six dimensions of smart, sustainable city research.

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Perisic, Marija Majda, Mario Štorga, and John S. Gero. "COMPUTATIONAL STUDY ON DESIGN SPACE EXPANSION DURING TEAMWORK." Proceedings of the Design Society 1 (July 27, 2021): 691–700. http://dx.doi.org/10.1017/pds.2021.69.

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AbstractWhen observing a design space expansion during teamwork, several studies found that cumulative solution-related issues' occurrence follows a linear trend. Such findings contradict the hypothesis of solution-related issues being characteristic for the later design stages. This work relies on agent-based simulations to explore the emerging patterns in design solution space expansion during teamwork. The results demonstrate trends that accord with the empirical findings, suggesting that a cognitive effort in solution space expansion remains constant throughout a design session. The collected data on agents' cognitive processes and solution space properties enabled additional insights, which led to the detection of four distinct regimes of design solution space expansion.

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Salunkhe, Uma R., and Suresh N. Mali. "Security Enrichment in Intrusion Detection System Using Classifier Ensemble." Journal of Electrical and Computer Engineering 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/1794849.

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In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique.

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Katsurai, Marie, and Shunsuke Ono. "TrendNets: mapping emerging research trends from dynamic co-word networks via sparse representation." Scientometrics 121, no. 3 (2019): 1583–98. http://dx.doi.org/10.1007/s11192-019-03241-6.

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Abstract Mapping the knowledge structure from word co-occurrences in a collection of academic papers has been widely used to provide insight into the topic evolution in an arbitrary research field. In a traditional approach, the paper collection is first divided into temporal subsets, and then a co-word network is independently depicted in a 2D map to characterize each period’s trend. To effectively map emerging research trends from such a time-series of co-word networks, this paper presents TrendNets, a novel visualization methodology that highlights the rapid changes in edge weights over time. Specifically, we formulated a new convex optimization framework that decomposes the matrix constructed from dynamic co-word networks into a smooth part and a sparse part: the former represents stationary research topics, while the latter corresponds to bursty research topics. Simulation results on synthetic data demonstrated that our matrix decomposition approach achieved the best burst detection performance over four baseline methods. In experiments conducted using papers published in the past 16 years at three conferences in different fields, we showed the effectiveness of TrendNets compared to the traditional co-word representation. We have made our codes available on the Web to encourage scientific mapping in all research fields.

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Schaffhauser, Andreas, Wojciech Mazurczyk, Luca Caviglione, Marco Zuppelli, and Julio Hernandez-Castro. "Efficient Detection and Recovery of Malicious PowerShell Scripts Embedded into Digital Images." Security and Communication Networks 2022 (June 29, 2022): 1–12. http://dx.doi.org/10.1155/2022/4477317.

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Due to steady improvements in defensive systems, malware developers are turning their attention to mechanisms for cloaking attacks as long as possible. A recent trend exploits techniques like Invoke-PSImage, which allows embedding a malicious script within an innocent-looking image, for example, to smuggle data into compromised devices. To address such a class of emerging threats, new mechanisms are needed, since standard tools fail in their detection or offer poor performance. To this aim, this work introduces Mavis, an efficient and highly accurate method for detecting hidden payloads, retrieving the embedded information, and estimating its size. Experimental results collected by considering real-world malicious PowerShell scripts showcase that Mavis can detect attacks with a high accuracy (100%) while keeping the rate of false positives and false negatives very low (0.01% and 0%, respectively). The proposed approach outperforms other solutions available in the literature or commercially through “as a service” model.

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