Journal articles on the topic 'Traces clustering'
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Greco, G., A. Guzzo, L. Pontieri, and D. Sacca. "Discovering expressive process models by clustering log traces." IEEE Transactions on Knowledge and Data Engineering 18, no. 8 (August 2006): 1010–27. http://dx.doi.org/10.1109/tkde.2006.123.
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Wu, Jianhong, Hossein Zivari-Piran, John D. Hunter, and John G. Milton. "Projective Clustering Using Neural Networks with Adaptive Delay and Signal Transmission Loss." Neural Computation 23, no. 6 (June 2011): 1568–604. http://dx.doi.org/10.1162/neco_a_00124.
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We develop a new neural network architecture for projective clustering of data sets that incorporates adaptive transmission delays and signal transmission information loss. The resultant selective output signaling mechanism does not require the addition of multiple hidden layers but instead is based on the assumption that the signal transmission velocity between input processing neurons and clustering neurons is proportional to the similarity between the input pattern and the feature vector (the top-down weights) of the clustering neuron. The mathematical model governing the evolution of the signal transmission delay, the short-term memory traces, and the long-term memory traces represents a new class of large-scale delay differential equations where the evolution of the delay is described by a nonlinear differential equation involving the similarity measure already noted. We give a complete description of the computational performance of the network for a wide range of parameter values.
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Gomez, Gibran, Platon Kotzias, Matteo Dell’Amico, Leyla Bilge, and Juan Caballero. "Unsupervised Detection and Clustering of Malicious TLS Flows." Security and Communication Networks 2023 (January 12, 2023): 1–17. http://dx.doi.org/10.1155/2023/3676692.
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Malware abuses TLS to encrypt its malicious traffic, preventing examination by content signatures and deep packet inspection. Network detection of malicious TLS flows is important, but it is a challenging problem. Prior works have proposed supervised machine learning detectors using TLS features. However, by trying to represent all malicious traffic, supervised binary detectors produce models that are too loose, thus introducing errors. Furthermore, they do not distinguish flows generated by different malware. On the other hand, supervised multiclass detectors produce tighter models and can classify flows by the malware family but require family labels, which are not available for many samples. To address these limitations, this work proposes a novel unsupervised approach to detect and cluster malicious TLS flows. Our approach takes input network traces from sandboxes. It clusters similar TLS flows using 90 features that capture properties of the TLS client, TLS server, certificate, and encrypted payload and uses the clusters to build an unsupervised detector that can assign a malicious flow to the cluster it belongs to, or determine if it is benign. We evaluate our approach using 972K traces from a commercial sandbox and 35M TLS flows from a research network. Our clustering shows very high precision and recall with an F1 score of 0.993. We compare our unsupervised detector with two state-of-the-art approaches, showing that it outperforms both. The false detection rate of our detector is 0.032% measured over four months of traffic.
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Cuzzocrea, Alfredo, Francesco Folino, Massimo Guarascio, and Luigi Pontieri. "Deviance-Aware Discovery of High-Quality Process Models." International Journal on Artificial Intelligence Tools 27, no. 07 (November 2018): 1860009. http://dx.doi.org/10.1142/s0218213018600096.
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Process Discovery techniques, allowing to extract graph-like models from large process logs, are a valuable mean for grasping a summarized view of real business processes’ behaviors. If augmented with statistics on process performances (e.g., processing times), such models help study the evolution of process performances across different processing steps, and possibly detect bottlenecks and worst practices. However, when the process analyzed exhibits complex and heterogeneous behaviors, these techniques fail to yield good quality models, in terms of readability, accuracy and generality. In particular, the presence of deviant traces may lead to cumbersome models and misleading performance statistics. Current noise/outlier filtering solutions can alleviate this problem and help discover a better model for “normal” process executions, but they do not provide insight on the deviant ones. Then, difficult and expensive analyses are usually performed to extract interpretable and general enough patterns for deviant behaviors. The performance-oriented discovery approach proposed here is addressed to recognize and describe both a normal execution scenario and deviant ones for the process analyzed, by inducing different sub-models: (i) a collection of readable clustering rules (conjunctive patterns over trace attributes) defining the deviance scenarios; (ii) a performance model [Formula: see text] for the “normal” traces that do not fall in any deviant scenario; and (iii) a performance model (and a “difference” model emphasizing the differences in behaviors from the “normal” execution scenario), for each discovered deviance scenario. Technically, these models are discovered by exploiting a conceptual clustering method, embedded in an iterative optimization scheme where the current version of [Formula: see text] is replaced with the model extracted from the newly found normality cluster, in case the latter is more accurate than [Formula: see text]; on the other hand, the clustering procedure is devised to greedily find groups of traces that maximally deviate from [Formula: see text]. Tests on real-life logs confirmed the validity of this approach, and its capability to find good performance models, and to support the analysis of deviant process instances.
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Dobrota, Milan, Boris Delibašić, and Pavlos Delias. "A Skiing Trace Clustering Model for Injury Risk Assessment." International Journal of Decision Support System Technology 8, no. 1 (January 2016): 56–68. http://dx.doi.org/10.4018/ijdsst.2016010104.
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This paper investigates the relation between skiing movement activity patterns and risk of injury. The goal is to provide a framework which can be used for estimating the level of skiers' injury risks, based on skiing patterns. Data, collected from ski-lift gates in the form of process event logs is analyzed. After initial transformation of data into traces, trace vectors, and similarity matrix, using several clustering methods different skiing patterns are identified and compared. The quality of clusters is determined by how well clusters discriminate between injured and noninjured skiers. The goal was to achieve the best possible discrimination. Several experimental settings were made to achieve and suggest a good combination of algorithm parameters and cluster number. After clusters are obtained, they are categorized in three categories according to risk level. It can be concluded that the proposed method can be used to distinguish skiing patterns by risk category based on injury occurrences.
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Dong, Zhenfen, Yuheng Men, Zhengming Li, Zhenzhen Liu, and Jianwei Ji. "Chilling Injury Segmentation of Tomato Leaves Based on Fluorescence Images and Improved k-Means++ Clustering." Transactions of the ASABE 64, no. 1 (2021): 13–22. http://dx.doi.org/10.13031/trans.13212.
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HighlightsChlorophyll fluorescence imaging can be used to evaluate chilling injury.Chilling injury area heterogeneity in the L*a*b* color space is significant.Improved k-means++ clustering has a good segmentation effect on chilling injury.Abstract. The application of fluorescence imaging in the detection of tomato chilling injury was investigated. With the segmentation of the chilling injury area serving as the experimental target, an algorithm based on chlorophyll fluorescence image analysis and improved k-means++ clustering was proposed. First, the extraction of lateral heterogeneity values algorithm was used to analyze the horizontal heterogeneity in five color spaces of the fluorescence images of tomato seedling leaves, and it was found that the chilling injury area was significant in the L*a*b* color space. Second, the fluorescence image was converted from the RGB color space to the L*a*b* color space, and the k-means++ algorithm was used to cluster the two-dimensional data of the a*b* space. Third, insertion sorting was used to reorder the different label regions obtained by the k-means++ clustering algorithm, and the region with the largest value was used as the target region. Finally, the binary image of the target region was filtered using a morphological noise filter, and the cold-damaged area was outputted by the mask operation. The results showed that the cold-damaged area was well segmented when the fluorescence imaging contained yellow cold traces. The mean match rate of the proposed algorithm was 37.08%, 13.52%, and 0.96% higher than that based on the HSV model and watershed algorithm, the fuzzy C-means clustering method, and the k-means clustering method, respectively. Similarly, the mean error rate was 13.69%, 5.56%, and 0.16% lower than that based on the HSV model and watershed algorithm, the fuzzy C-means clustering method, and the k-means clustering method, respectively. These findings provide a foundation for research on early warning of chilling injury by identifying the chilling injury status of tomato leaves using a computer vision method. Keywords: Chlorophyll fluorescence, Fluorescence image, Image segmentation, k-Means++.
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Chang, Xiangmao, Quan Wang, Zhiguo Qu, and Yanchao Zhao. "The integration of compressive sensing and clustering for date gathering in unmanned aircraft system–aided networks." International Journal of Distributed Sensor Networks 13, no. 8 (August 2017): 155014771772771. http://dx.doi.org/10.1177/1550147717727713.
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The development of the unmanned aircraft systems is envisioned to greatly reduce the energy consumption of sensor nodes in data gathering process using unmanned aircraft systems as mobile sinks. In traditional sensor networks, compressive sensing and clustering are two key energy-efficient techniques for data gathering. However, how to integrate two techniques into the data gathering for unmanned aircraft system–aided wireless sensor networks effectively is still an open problem. Moreover, most clustering schemes focus on the cluster head selection strategy and simplified the problem of cluster member selection, and most compressive sensing schemes are not integrated with the clustering strategy. To this end, this article studies the problem of integrating compressive sensing with clustering for data gathering in unmanned aircraft system–aided networks. We first give a theoretical formulation of this problem. Considering the non-deterministic polynomial-time hard complexity of the problem, we present two algorithms by jointly considering the compressive ratio variation factor and the distance factor to find near-optimal solutions heuristically. Evaluations based on real data traces show that the proposed algorithms greatly reduced the energy consumption of sensor nodes efficiency.
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NAKAZATO, Junji, Jungsuk SONG, Masashi ETO, Daisuke INOUE, and Koji NAKAO. "A Novel Malware Clustering Method Using Frequency of Function Call Traces in Parallel Threads." IEICE Transactions on Information and Systems E94-D, no. 11 (2011): 2150–58. http://dx.doi.org/10.1587/transinf.e94.d.2150.
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N, Pushpalatha M., and runalini M. "Clustering The Duplicate Open Crash Reports Based on Call Stack Traces of Crash Reports." International Journal of Computer Sciences and Engineering 6, no. 9 (September 30, 2018): 207–10. http://dx.doi.org/10.26438/ijcse/v6i9.207210.
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Rojas, Alexis, Gregory P. Dietl, Michał Kowalewski, Roger W. Portell, Austin Hendy, and Jason K. Blackburn. "Spatial point pattern analysis of traces (SPPAT): An approach for visualizing and quantifying site-selectivity patterns of drilling predators." Paleobiology 46, no. 2 (May 2020): 259–71. http://dx.doi.org/10.1017/pab.2020.15.
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AbstractSite-selectivity analysis of drilling predation traces may provide useful behavioral information concerning a predator interacting with its prey. However, traditional approaches exclude some spatial information (i.e., oversimplified trace position) and are dependent on the scale of analysis (e.g., arbitrary grid system used to divide the prey skeleton into sectors). Here we introduce the spatial point pattern analysis of traces (SPPAT), an approach for visualizing and quantifying the distribution of traces on shelled invertebrate prey, which includes improved collection of spatial information inherent to drillhole location (morphometric-based estimation), improved visualization of spatial trends (kernel density and hotspot mapping), and distance-based statistics for hypothesis testing (K-, L-, and pair correlation functions). We illustrate the SPPAT approach through case studies of fossil samples, modern beach-collected samples, and laboratory feeding trials of naticid gastropod predation on bivalve prey. Overall results show that kernel density and hotspot maps enable visualization of subtle variations in regions of the shell with higher density of predation traces, which can be combined with the maximum clustering distance metric to generate hypotheses on predatory behavior and anti-predatory responses of prey across time and geographic space. Distance-based statistics also capture the major features in the distribution of traces across the prey skeleton, including aggregated and segregated clusters, likely associated with different combinations of two modes of drilling predation, edge and wall drilling. The SPPAT approach is transferable to other paleoecologic and taphonomic data such as encrustation and bioerosion, allowing for standardized investigation of a wide range of biotic interactions.
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Matthews, Brett A., and Mark A. Clements. "Spike Sorting by Joint Probabilistic Modeling of Neural Spike Trains and Waveforms." Computational Intelligence and Neuroscience 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/643059.
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This paper details a novel probabilistic method for automatic neural spike sorting which uses stochastic point process models of neural spike trains and parameterized action potential waveforms. A novel likelihood model for observed firing times as the aggregation of hidden neural spike trains is derived, as well as an iterative procedure for clustering the data and finding the parameters that maximize the likelihood. The method is executed and evaluated on both a fully labeled semiartificial dataset and a partially labeled real dataset of extracellular electric traces from rat hippocampus. In conditions of relatively high difficulty (i.e., with additive noise and with similar action potential waveform shapes for distinct neurons) the method achieves significant improvements in clustering performance over a baseline waveform-only Gaussian mixture model (GMM) clustering on the semiartificial set (1.98% reduction in error rate) and outperforms both the GMM and a state-of-the-art method on the real dataset (5.04% reduction in false positive + false negative errors). Finally, an empirical study of two free parameters for our method is performed on the semiartificial dataset.
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Schulte, Johannes P., Felipe T. Giuntini, Renato A. Nobre, Khalil C. do Nascimento, Rodolfo I. Meneguette, Weigang Li, Vinícius P. Gonçalves, and Geraldo P. Rocha Filho. "ELINAC: Autoencoder Approach for Electronic Invoices Data Clustering." Applied Sciences 12, no. 6 (March 16, 2022): 3008. http://dx.doi.org/10.3390/app12063008.
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The most common method used to document monetary transactions in Brazil is by issuing electronic invoices (NF-e). The audit of electronic invoices is essential, and this can be improved by using data mining solutions, such as clustering and anomaly detection. However, applying these solutions is not a simple task because NF-e data contains millions of records with noisy fields and nonstandard documents, especially short text descriptions. In addition to these challenges, it is costly to extract information from short texts to identify traces of mismanagement, embezzlement, commercial fraud or tax evasion. Analyzing such data can be more effective when divided into well-defined groups. However, efficient solutions for clustering data with characteristics similar to NF-es have not yet been proposed in the literature. We developed ELINAC, a service for clustering short-text data in NF-es that uses an automatic encoder to cluster data. ELINAC aids in auditing transactions documented in NF-e, clustering similar data by short-text descriptions and making anomaly detection in numeric fields easier. For this, ELINAC explores how to model the automatic encoder without increasing the calculation costs to suppress a large number of short text data. In the worst case, the results show that ELINAC efficiently groups data while performing three times faster than solutions previously adopted in the literature.
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Myrtennäs, Kerstin, Raquel Escudero, Ángel Zaballos, Rosa González-Martín-Niño, Miklós Gyuranecz, and Anders Johansson. "Genetic Traces of the Francisella tularensis Colonization of Spain, 1998–2020." Microorganisms 8, no. 11 (November 14, 2020): 1784. http://dx.doi.org/10.3390/microorganisms8111784.
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More than 1000 humans have acquired the febrile disease tularemia in Spain since the first notification of human cases in 1997. We here aimed to study the recent molecular evolution of the causative bacterium Francisella tularensis during disease establishment in Spain. Single-nucleotide polymorphisms (SNPs) and variable-number tandem repeats (VNTRs) were analyzed in whole-genome sequences (WGS) of F. tularensis. Short-read WGS data for 20 F. tularensis strains from humans infected in the periods 2014–2015 and 2018–2020 in Spain were generated. These data were combined with WGS data of 25 Spanish strains from 1998 to 2008 and two reference strains. Capillary electrophoresis data of VNTR genetic regions were generated and compared with the WGS data for the 11 strains from 2014 to 2015. Evolutionary relationships among strains were analyzed by phylogenetic methods. We identified 117 informative SNPs in a 1,577,289-nucleotide WGS alignment of 47 F. tularensis genomes. Forty-five strains from Spain formed a star-like SNP phylogeny with six branches emerging from a basal common node. The most recently evolved genomes formed four additional star-like structures that were derived from four branches of the basal common node. VNTR copy number variation was detected in two out of 10 VNTR regions examined. Genetic clustering of strains by VNTRs agreed with the clustering by SNPs. The SNP data provided higher resolution among strains than the VNTRs data in all but one cases. There was an excellent correlation between VNTR marker sizing by capillary electrophoresis and prediction from WGS data. The genetic data strongly support that tularemia, indeed, emerged recently in Spain. Distinct genetic patterns of local F. tularensis population expansions imply that the pathogen has colonized a previously disease-free geographical area. We also found that genome-wide SNPs provide higher genetic resolution among F. tularensis genomes than the use of VNTRs, and that VNTR copy numbers can be accurately predicted using short-read WGS data.
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Wang, Yun, Hua-Yu Yang, and Ping He. "Continuous Wavelet Analysis of Matter Clustering Using the Gaussian-derived Wavelet." Astrophysical Journal 934, no. 1 (July 1, 2022): 77. http://dx.doi.org/10.3847/1538-4357/ac752c.
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Abstract Continuous wavelet analysis has been increasingly employed in various fields of science and engineering due to its remarkable ability to maintain optimal resolution in both space and scale. Here, we introduce wavelet-based statistics, including the wavelet power spectrum, wavelet cross correlation, and wavelet bicoherence, to analyze the large-scale clustering of matter. For this purpose, we perform wavelet transforms on the density distribution obtained from the one-dimensional Zel’dovich approximation and then measure the wavelet power spectra and wavelet bicoherences of this density distribution. Our results suggest that the wavelet power spectrum and wavelet bicoherence can identify the effects of local environments on the clustering at different scales. Moreover, we apply the statistics based on the three-dimensional isotropic wavelet to the IllustrisTNG simulation at z = 0, and investigate the environmental dependence of the matter clustering. We find that the clustering strength of the total matter increases with increasing local density except on the largest scales. Besides, we notice that the gas traces dark matter better than stars on large scales in all environments. On small scales, the cross correlation between the dark matter and gas first decreases and then increases with increasing density. This is related to the impacts of the active galactic nucleus feedback on the matter distribution, which also varies with the density environment in a similar trend to the cross correlation between dark matter and gas. Our findings are qualitatively consistent with previous studies on matter clustering.
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Nguyen, Cong-Binh, Seokhoon Yoon, and Jangyoung Kim. "Discovering Social Community Structures Based on Human Mobility Traces." Mobile Information Systems 2017 (2017): 1–17. http://dx.doi.org/10.1155/2017/2190310.
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We consider a community detection problem in a social network. A social network is composed of smaller communities; that is, a society can be partitioned into different social groups in which the members of the same group maintain stronger and denser social connections than individuals from different groups. In other words, people in the same community have substantially interdependent social characteristics, indicating that the community structure may facilitate understanding human interactions as well as individual’s behaviors. We detect the social groups within a network of mobile users by analyzing the Bluetooth-based encounter history from a real-life mobility dataset. Our community detection methodology focuses on designing similarity measurements that can reflect the degree of social connections between users by considering tempospatial aspects of human interactions, followed by clustering algorithms. We also present two evaluation methods for the proposed schemes. The first method relies on the natural properties of friendship, where the longevity, frequency, and regularity characteristics of human encounters are considered. The second is a movement-prediction-based method which is used to verify the social ties between users. The evaluation results show that the proposed schemes can achieve high performance in detecting the social community structure.
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HIRANO, SHOJI, and SHUSAKU TSUMOTO. "MULTISCALE COMPARISON AND CLUSTERING OF THREE-DIMENSIONAL TRAJECTORIES BASED ON CURVATURE MAXIMA." International Journal of Information Technology & Decision Making 09, no. 06 (November 2010): 889–904. http://dx.doi.org/10.1142/s021962201000410x.
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This paper presents a multiscale comparison method for three-dimensional trajectories. In order to deal with the problem that zero-crossings of curvature cannot be determined for space curve, we utilize the maxima of curvature. The method first traces the positions of curvature maxima across scales for recognizing the hierarchy of partial trajectories. Then it performs cross-scale matching of partial trajectories derived from two input trajectories, and obtains the structurally best matches. Finally, it calculates the value-based dissimilarity for each pair of the matched partial trajectories and output as the final dissimilarity between trajectories that can be further used for clustering or classification tasks. In experiments on the UCI character trajectory dataset we demonstrate that reasonable correspondences were captured successfully and the derived dissimilarity yielded good clustering results comparable to DTW. We also demonstrate using real medical data that the method could generate interesting clusters that might reflect distribution of fibrotic stages.
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Stephen, Donna L. "A Discussion of Avery Weisman's Notion of Appropriate Death." OMEGA - Journal of Death and Dying 24, no. 4 (June 1992): 301–8. http://dx.doi.org/10.2190/8c1x-phtd-45ed-kykx.
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This article traces uses of the term “appropriate death,” as introduced by Avery Weisman in 1970, and some of the term's philosophic difficulties. It is concluded that “appropriate death” has been used to refer to a clustering of three components: 1) consistency in functioning; 2) idiosyncratic views of appropriate; and 3) features which contribute toward a better death. It is then argued that the core concept-the one which gives the term special usefulness-is an emphasis on the idiosyncratic. Comments concerning theoretical implications of “appropriate dying” are discussed relative to the concepts of living will and euthanasia.
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Fernández, Carina, José Bavio, and Beatriz Marrón. "Evaluation of Clustering Techniques to Estimate the Effective Bandwidth of a Markovian Fluid from Traffic Traces." IEEE Latin America Transactions 21, no. 5 (May 2023): 636–42. http://dx.doi.org/10.1109/tla.2023.10130835.
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Kreisch, Christina D., Alice Pisani, Carmelita Carbone, Jia Liu, Adam J. Hawken, Elena Massara, David N. Spergel, and Benjamin D. Wandelt. "Massive neutrinos leave fingerprints on cosmic voids." Monthly Notices of the Royal Astronomical Society 488, no. 3 (July 17, 2019): 4413–26. http://dx.doi.org/10.1093/mnras/stz1944.
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ABSTRACT Do void statistics contain information beyond the tracer 2-point correlation function? Yes! As we vary the sum of the neutrino masses, we find void statistics contain information absent when using just tracer 2-point statistics. Massive neutrinos uniquely affect cosmic voids. We explore their impact on void clustering using both the DEMNUni and MassiveNuS simulations. For voids, neutrino effects depend on the observed void tracers. As the neutrino mass increases, the number of small voids traced by cold dark matter particles increases and the number of large voids decreases. Surprisingly, when massive, highly biased, haloes are used as tracers, we find the opposite effect. The scale at which voids cluster, as well as the void correlation, is similarly sensitive to the sum of neutrino masses and the tracers. This scale-dependent trend is not due to simulation volume or halo density. The interplay of these signatures in the void abundance and clustering leaves a distinct fingerprint that could be detected with observations and potentially help break degeneracies between different cosmological parameters. This paper paves the way to exploit cosmic voids in future surveys to constrain the mass of neutrinos.
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Ranacher, Peter, Nico Neureiter, Rik van Gijn, Barbara Sonnenhauser, Anastasia Escher, Robert Weibel, Pieter Muysken, and Balthasar Bickel. "Contact-tracing in cultural evolution: a Bayesian mixture model to detect geographic areas of language contact." Journal of The Royal Society Interface 18, no. 181 (August 2021): 20201031. http://dx.doi.org/10.1098/rsif.2020.1031.
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When speakers of different languages interact, they are likely to influence each other: contact leaves traces in the linguistic record, which in turn can reveal geographical areas of past human interaction and migration. However, other factors may contribute to similarities between languages. Inheritance from a shared ancestral language and universal preference for a linguistic property may both overshadow contact signals. How can we find geographical contact areas in language data, while accounting for the confounding effects of inheritance and universal preference? We present sBayes , an algorithm for Bayesian clustering in the presence of confounding effects. The algorithm learns which similarities are better explained by confounders, and which are due to contact effects. Contact areas are free to take any shape or size, but an explicit geographical prior ensures their spatial coherence. We test sBayes on simulated data and apply it in two case studies to reveal language contact in South America and the Balkans. Our results are supported by findings from previous studies. While we focus on detecting language contact, the method can also be used to uncover other traces of shared history in cultural evolution, and more generally, to reveal latent spatial clusters in the presence of confounders.
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Aitdaoud, Mohammed, Abdelwahed Namir, and Mohammed Talbi. "New Pre-Processing Approach Based on Clustering Users Traces According to their Learning Styles in Moodle LMS." International Journal of Emerging Technologies in Learning (iJET) 18, no. 07 (April 5, 2023): 226–42. http://dx.doi.org/10.3991/ijet.v18i07.37635.
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Nowadays, many Moroccan universities and institutions start offering training and online courses "E-learning". Which accumulate a vast amount of information that is very valuable for analyzing students’ behavior and could create a gold mine of educational data. However, handling the vast quantities of data generated daily by the learning management systems (LMS) such as Moodle has become more and more complicated. This massive data can be used to improve decision making and management, which requires a proper extracting and cleaning methods.
The purpose of this paper is to suggest a new approach for the preprocessing of the execution traces generated during the interaction of learners with the Moodle LMS and especially the educational content in SCORM format.
In this study, we built two experimental corpus with the learning platform Moodle. Using the data generated by the experimental corpus, we applied the Clustering data mining technique as a preprocessing step in our process discovery. Hence, students with similar learning styles or performance levels are grouped together which should help us to build a partial process model (learning process) that are easier to understand for the decision makers.
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Chiarion, Giovanni, and Luca Mesin. "Functional Connectivity of EEG in Encephalitis during Slow Biphasic Complexes." Electronics 10, no. 23 (November 30, 2021): 2978. http://dx.doi.org/10.3390/electronics10232978.
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The electroencephalogram (EEG) of patients suffering from inflammatory diseases of the brain may show specific waveforms called slow biphasic complexes (SBC). Recent studies indicated a correlation between the severity of encephalitis and some features of SBCs, such as location, amplitude and frequency of appearance. Moreover, EEG rhythms were found to vary before the onset of an SBC, as if the brain was preparing to the discharge (actually with a slowing down of the EEG oscillation). Here, we investigate possible variations of EEG functional connectivity (FC) in EEGs from pediatric patients with different levels of severity of encephalitis. FC was measured by the maximal crosscorrelation of EEG rhythms in different bipolar channels. Then, the indexes of network patterns (namely strength, clustering coefficient, efficiency and characteristic path length) were estimated to characterize the global behavior when they are measured during SBCs or far from them. EEG traces showed statistical differences in the two conditions: clustering coefficient, efficiency and strength are higher close to an SBC, whereas the characteristic path length is lower. Moreover, for more severe conditions, an increase in clustering coefficient, efficiency and strength and a decrease in characteristic path length were observed in the delta–theta band. These outcomes support the hypothesis that SBCs result from the anomalous coordination of neurons in different brain areas affected by the inflammation process and indicate FC as an additional key for interpreting the EEG in encephalitis patients.
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Apostolo, Guilherme Henrique, Flavia Bernardini, Luiz C. Schara Magalhães, and Débora C. Muchaluat-Saade. "eSCIFI: An Energy Saving Mechanism for WLANs Based on Machine Learning." Energies 15, no. 2 (January 10, 2022): 462. http://dx.doi.org/10.3390/en15020462.
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As wireless local area networks grow in size to provide access to users, power consumption becomes an important issue. Power savings in a large-scale Wi-Fi network, with low impact to user service, is undoubtedly desired. In this work, we propose and evaluate the eSCIFI energy saving mechanism for Wireless Local Area Networks (WLANs). eSCIFI is an energy saving mechanism that uses machine learning algorithms as occupancy demand estimators. The eSCIFI mechanism is designed to cope with a broader range of WLANs, which includes Wi-Fi networks such as the Fluminense Federal University (UFF) SCIFI network. The eSCIFI can cope with WLANs that cannot acquire data in a real time manner and/or possess a limited CPU power. The eSCIFI design also includes two clustering algorithms, named cSCIFI and cSCIFI+, that help to guarantee the network’s coverage. eSCIFI uses those network clusters and machine learning predictions as input features to an energy state decision algorithm that then decides which Access Points (AP) can be switched off during the day. To evaluate eSCIFI performance, we conducted several trace-driven simulations comparing the eSCIFI mechanism using both clustering algorithms with other energy saving mechanisms found in the literature using the UFF SCIFI network traces. The results showed that eSCIFI mechanism using the cSCIFI+ clustering algorithm achieves the best performance and that it can save up to 64.32% of the UFF SCIFI network energy without affecting the user coverage.
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Chen, J., T. Hu, P. Zhang, W. Shi, and J. Shan. "Trajectory Clustering for People's Movement Pattern Based on Crowd Souring Data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2 (November 11, 2014): 55–62. http://dx.doi.org/10.5194/isprsarchives-xl-2-55-2014.
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With the increasing availability of GPS-enabled devices, a huge amount of GPS trajectories recording people's location traces have been accumulated and shared freely on the Web. In this area, one of the most important research topics is to exploit trajectory-movement pattern about where and when people clustered based on the raw GPS data. In order to solve this problem, clustering is a good way to perform data mining tasks on trajectory data. <br><br> This paper provides a clustering algorithm which aims at mining people’s movement pattern about the clustered location and their temporal evolution characteristics. Firstly, the characteristic points of GPS trajectories were chosen. Based on the characteristic points, a trajectory has been partitioned into a group of line segments. These line segments can represent the movement pattern of trajectories much better than that of track points. Secondly, an improved density-based line clustering method was used for the individual partitioned line segments to find out individual clusters with similar track segments. In this step, the absolute time spot of people’s trajectories was taking into account as a characteristic for the temporal evolution of people’s trajectories. Finally, the representative clustered hot spots of multiple users’ line segments achieved by above steps were output. Experiments were conducted with GPS trajectories data downloaded from the web to verify the effectiveness of the algorithm in this paper. According to the results, the spatial distribution and temporal evolution characteristics of people’s stay hot spots were effectively discovered from people’s GPS trajectories data.
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Benabbes, Khalid, Khalid Housni, Ahmed Zellou, Hmedna Brahim, and Ali El Mezouary. "Context and Learning Style Aware Recommender System for Improving the E-Learning Environment." International Journal of Emerging Technologies in Learning (iJET) 18, no. 09 (May 10, 2023): 180–202. http://dx.doi.org/10.3991/ijet.v18i09.38361.
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The learning management system (LMS) is an e-learning software that raised the interest of disparate learners’ groups. However, learners have difficulties in finding learning resources tailored to their preferences in the best way at the right time. Making the learning process more efficient and pleasant for learners can be achieved by using context and learning styles such as customizing aspects. This study proposes a new data-driven approach to retrieve learners' characteristics using traces of their activities based on the Felder-Silverman Learning Style Model (FSLSM). In this research, the traces of 714 learners who enrolled in three agronomy courses taught at IAV HASSAN II (winter session 2019, 2020, and 2021) were analyzed. Learners are categorized into clusters by their preference level for global/sequential learning styles, using an unsupervised clustering method. Then a classifier model tailored to our requirements was trained and based on the learner's learning style and their current context, a learning object recommendation list is proposed for them. The results revealed that the k-means algorithm performed well in identifying learning styles (LS) and the use of context features defined from the learners' adaptive close environments improved learning performance with an accuracy of over 96% given that most of the learners preferred a global learning style.
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Cavazza, Nicoletta, and Piergiorgio Corbetta. "The political meaning of dining out: testing the link between lifestyle and political choice in Italy." Italian Political Science Review/Rivista Italiana di Scienza Politica 46, no. 1 (October 20, 2015): 23–45. http://dx.doi.org/10.1017/ipo.2015.24.
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The debate that has arisen around the weakening of the traditional cleavages’ heuristic power in explaining vote suggests considering the role of lifestyles in designing politically meaningful social aggregates. We investigated the relationship between lifestyle and voting behavior, establishing the degree to which this relationship traces the effect of the socio-structural categories (e.g. social class) or is, at least in part, independent of them. Through a k-means clustering, we individuated a typology of four Italian lifestyles; we showed its relation to socio-demographic features and its ability to discriminate participants’ political attitudes. The subscription to each lifestyle was significantly associated with voting behavior, net of the variance accounted for by the traditional cleavages. The theoretical implication and further direction of research are discussed.
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Diggelmann, Roland, Michele Fiscella, Andreas Hierlemann, and Felix Franke. "Automatic spike sorting for high-density microelectrode arrays." Journal of Neurophysiology 120, no. 6 (December 1, 2018): 3155–71. http://dx.doi.org/10.1152/jn.00803.2017.
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High-density microelectrode arrays can be used to record extracellular action potentials from hundreds to thousands of neurons simultaneously. Efficient spike sorters must be developed to cope with such large data volumes. Most existing spike sorting methods for single electrodes or small multielectrodes, however, suffer from the “curse of dimensionality” and cannot be directly applied to recordings with hundreds of electrodes. This holds particularly true for the standard reference spike sorting algorithm, principal component analysis-based feature extraction, followed by k-means or expectation maximization clustering, against which most spike sorters are evaluated. We present a spike sorting algorithm that circumvents the dimensionality problem by sorting local groups of electrodes independently with classical spike sorting approaches. It is scalable to any number of recording electrodes and well suited for parallel computing. The combination of data prewhitening before the principal component analysis-based extraction and a parameter-free clustering algorithm obviated the need for parameter adjustments. We evaluated its performance using surrogate data in which we systematically varied spike amplitudes and spike rates and that were generated by inserting template spikes into the voltage traces of real recordings. In a direct comparison, our algorithm could compete with existing state-of-the-art spike sorters in terms of sensitivity and precision, while parameter adjustment or manual cluster curation was not required. NEW & NOTEWORTHY We present an automatic spike sorting algorithm that combines three strategies to scale classical spike sorting techniques for high-density microelectrode arrays: 1) splitting the recording electrodes into small groups and sorting them independently; 2) clustering a subset of spikes and classifying the rest to limit computation time; and 3) prewhitening the spike waveforms to enable the use of parameter-free clustering. Finally, we combined these strategies into an automatic spike sorter that is competitive with state-of-the-art spike sorters.
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Talavera-Mendoza, Fabiola, Carlos E. Atencio-Torres, Henry del Carpio, David A. Deza, and Alexander R. Cayro. "Usability Analysis and Clustering Model in e-Learning from the User Experience Perspective." International Journal of Information and Education Technology 12, no. 2 (2022): 108–15. http://dx.doi.org/10.18178/ijiet.2022.12.2.1593.
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Online learning offers opportunities responding to their different individual and group learning needs by leaving digital traces that allow tracking their experiences at the user level. This study aims to examine the perceived usability of the gamified educational platform called (ELORS) in relation to online behaviour. As well as analyse the clustering models in terms of their high and low level of engagement through their interaction metrics. A quantitative, descriptive correlational approach and an educational data analysis design was adopted through the K-means algorithm. The participants were 51 students in mathematics in the second year of secondary education. An instrument was used to evaluate usability and behavioural metrics, analysing 1065 interactions with 57 activities. The results showed advantages in usability and grouping. The level of usability achieved depends on the interaction of the users with the different learning objects and their moderate relationship in their interactions. In relation to the centroids, two groups are evidenced by number of attempts and interactions, identifying students with low levels of participation in the minority. A significant finding is given in relation to the preference of redeeming virtual values in gold from the diamonds collected. The perspective of the analysis allows identifying the potential of the gamified platform to work online in the formation of mathematical competence according to the current educational curriculum.
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Perez, Lucia A., Sangeeta Malhotra, James E. Rhoads, Peter Laursen, and Isak G. B. Wold. "Probing Patchy Reionization with the Void Probability Function of Lyα Emitters." Astrophysical Journal 940, no. 2 (November 24, 2022): 102. http://dx.doi.org/10.3847/1538-4357/ac9b57.
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Abstract We probe what constraints for the global ionized hydrogen fraction the void probability function (VPF) clustering can give for the Lyman Alpha Galaxies in the Epoch of Reionization (LAGER) narrowband survey as a function of area. Neutral hydrogen acts like a fog for Lyα emission, and measuring the drop in the luminosity function of Lyα emitters (LAEs) has been used to constrain the ionization fraction in narrowband surveys. However, the clustering of LAEs is independent of the luminosity function’s inherent evolution, and can offer additional constraints for reionization under different models. The VPF measures how likely a given circle is to be empty. It is a volume-averaged clustering statistic that traces the behavior of higher-order correlations, and its simplicity offers helpful frameworks for planning surveys. Using the Jensen et al. simulations of LAEs within various amounts of ionized intergalactic medium, we predict the behavior of the VPF in one (301 × 150.5 × 30 Mpc3), four (5.44 × 106 Mpc3), or eight (1.1 × 107 Mpc3) fields of LAGER imaging. We examine the VPF at 5′ and 13′, corresponding to the minimum scale implied by the LAE density and the separation of the 2D VPF from random, and the maximum scale from the eight-field 15.5 deg2 LAGER area. We find that even a single DECam field of LAGER (2–3 deg2) could discriminate between mostly neutral versus ionized. Additionally, we find four fields allow for the distinction between 30%, 50%, and 95% ionized, and eight fields could even distinguish between 30%, 50%, 73%, and 95% ionized.
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ŞAHİN, Fatih. "Türkiye Otomotiv Sanayiinin Yenilikçilik Potansiyeli: Uluslararası Yatırımlar, Coğrafi Kümelenme ve Alıcı-Tedarikçi İlişkileri Açılarından Bir Değerlendirme." Fiscaoeconomia 7, no. 2 (May 25, 2023): 1486–510. http://dx.doi.org/10.25295/fsecon.1263652.
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The aim of this study is to provide information to researchers by examining the basic norms of international investment activities, geographical clustering and R&D and innovation activities, which are thought to affect the innovation potential of the Turkish automotive industry. The evaluations made within the scope of the study are based on the observations and secondary data sources that emerged as the result of large-scale field studies carried out by the researcher. As a result of these evaluations, it has been revealed that the development of the sector, which historically bears the traces of late industrialization, has parallel features with the development of the Turkish Business System and that foreign-owned organizations have an absolute role in the historical development of the Turkish automotive industry. In addition, it is seen that export and outward FDI activities, which constitute one of the pillars of innovation, are carried out effectively in the sector. The hierarchical supply chain structure, which is shaped within the unique dynamics of the sector, is considered to constitute a remarkable field of study in terms of innovation potential. Finally, the geographical clustering feature of the sector in terms of spreading and institutionalizing innovation among organizations also constitutes an important incentive factor.
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Wei, Jie, Ao Zhou, Jie Yuan, and Fangchun Yang. "AIMING: Resource Allocation with Latency Awareness for Federated-Cloud Applications." Wireless Communications and Mobile Computing 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/4593208.
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Federated-cloud has been widely deployed due to the growing popularity of real-time applications, and hence allocating resources among clouds becomes nontrivial to meet the stringent service requirements. The challenges lie in achieving minimized latency constrained by virtual machines rental overhead and resource requirement. This becomes further complicated by the issues of datacenter selection. To this end, we propose AIMING, a novel resource allocation approach which aims to minimize the latency constrained by monetary overhead in the context of federated-cloud. Specifically, the network resources are deployed and selected according to k-means clustering. Meanwhile, the total latency among datacenters is optimized based on binary quadratic programming. The evaluation is conducted with real data traces. The results show that AIMING can reduce total datacenter latency effectively compared with other approaches.
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Yuan, Jinghe, Kangmin He, Ming Cheng, Jianqiang Yu, and Xiaohong Fang. "Analysis of the Steps in Single-Molecule Photobleaching Traces by Using the Hidden Markov Model and Maximum-Likelihood Clustering." Chemistry - An Asian Journal 9, no. 8 (June 30, 2014): 2303–8. http://dx.doi.org/10.1002/asia.201402147.
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Chan, Kwan Chuen, Ismael Ferrero, Santiago Avila, Ashley J. Ross, Martin Crocce, and Enrique Gaztañaga. "Clustering with general photo-z uncertainties: application to Baryon Acoustic Oscillations." Monthly Notices of the Royal Astronomical Society 511, no. 3 (February 10, 2022): 3965–82. http://dx.doi.org/10.1093/mnras/stac340.
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ABSTRACT Photometric data can be analysed using the 3D correlation function ξp to extract cosmological information via e.g. measurement of the Baryon Acoustic Oscillations (BAO). Previous studies modeled ξp assuming a Gaussian photo-z approximation. In this work we improve the modeling by incorporating realistic photo-z distribution. We show that the position of the BAO scale in ξp is determined by the photo-z distribution and the Jacobian of the transformation. The latter diverges at the transverse scale of the separation s⊥, and it explains why ξp traces the underlying correlation function at s⊥, rather than s, when the photo-z uncertainty σz/(1+ z) ≳ 0.02. We also obtain the Gaussian covariance for ξp. Due to photo-z mixing, the covariance of ξp shows strong off-diagonal elements. The high correlation of the data causes some issues to the data fitting. None the less, we find that either it can be solved by suppressing the largest eigenvalues of the covariance or it is not directly related to the BAO. We test our BAO fitting pipeline using a set of mock catalogs. The data set is dedicated for Dark Energy Survey Year 3 (DES Y3) BAO analyses and includes realistic photo-z distributions. The theory template is in good agreement with mock measurement. Based on the DES Y3 mocks, ξp statistic is forecast to constrain the BAO shift parameter α to be 1.001 ± 0.023, which is well consistent with the corresponding constraint derived from the angular correlation function measurements. Thus, ξp offers a competitive alternative for the photometric data analyses.
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Yang, Xiaodong, Omar Ali Beg, Matthew Kenigsberg, and Taylor T. Johnson. "A Framework for Identification and Validation of Affine Hybrid Automata from Input-Output Traces." ACM Transactions on Cyber-Physical Systems 6, no. 2 (April 30, 2022): 1–24. http://dx.doi.org/10.1145/3470455.
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Automata-based modeling of hybrid and cyber-physical systems (CPS) is an important formal abstraction amenable to algorithmic analysis of its dynamic behaviors, such as in verification, fault identification, and anomaly detection. However, for realistic systems, especially industrial ones, identifying hybrid automata is challenging, due in part to inferring hybrid interactions, which involves inference of both continuous behaviors, such as through classical system identification, as well as discrete behaviors, such as through automata (e.g., L*) learning. In this paper, we propose and evaluate a framework for inferring and validating models of deterministic hybrid systems with linear ordinary differential equations (ODEs) from input/output execution traces. The framework contains algorithms for the approximation of continuous dynamics in discrete modes, estimation of transition conditions, and the inference of automata mode merging. The algorithms are capable of clustering trace segments and estimating their dynamic parameters, and meanwhile, deriving guard conditions that are represented by multiple linear inequalities. Finally, the inferred model is automatically converted to the format of the original system for the validation. We demonstrate the utility of this framework by evaluating its performance in several case studies as implemented through a publicly available prototype software framework called HAutLearn and compare it with a membership-based algorithm.
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Nayef AL-Dabagh, Mustafa Zuhaer. "Automated tumor segmentation in MR brain image using fuzzy c-means clustering and seeded region methodology." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (June 1, 2021): 284. http://dx.doi.org/10.11591/ijai.v10.i2.pp284-290.
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<span id="docs-internal-guid-c8cba487-7fff-2314-f38a-f2936a74e0fd"><span>Automated segmentation of a tumor is still a considerably exciting research topic in the medical imaging processing field, and it plays a considerable role in forming a right diagnosis, to aid effective medical treatment. In this work, a fully automated system for segmentation of the brain tumor in MRI images is introduced. The suggested system consists of three parts: Initially, the image is pre-processed to enhance contrast, eliminate noise, and strip the skull from the image using filtering and morphological operations. Secondly, segmentation of the image happens using two techniques, fuzzy c-means clustering (FCM) and with the application of a seeded region growing algorithm (SGR). Thirdly, this method proposes a post-processing step to smooth segmentation region edges using morphological operations. The testing of the proposed system involved 233 patients, which included 287 MRI images. A comparison of the results ensued, with the manual verification of the traces performed by doctors, which ultimately proved an average Dice Coefficient of 90.13% and an average Jaccard Coefficient of 82.60% also, by comparison with traditional segmentation techniques such as FCM method. The segmentation results and quantitative data analysis demonstrates the effectiveness of the suggested system.</span></span>
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García-Vergara, Cristina, Matus Rybak, Jacqueline Hodge, Joseph F. Hennawi, Roberto Decarli, Jorge González-López, Fabrizio Arrigoni-Battaia, Manuel Aravena, and Emanuele P. Farina. "ALMA Reveals a Large Overdensity and Strong Clustering of Galaxies in Quasar Environments at z ∼ 4." Astrophysical Journal 927, no. 1 (March 1, 2022): 65. http://dx.doi.org/10.3847/1538-4357/ac469d.
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Abstract We present an Atacama Large Millimeter/submillimeter Array (ALMA) survey of CO(4–3) line emitting galaxies in 17 quasar fields at z ∼ 4 aimed at performing the first systematic search of dusty galaxies in high-z quasar environments. Our blind search of galaxies around the quasars results in five CO emitters with S/N ≥ 5.6 within a projected radius of R ≲ 1.5 h −1 cMpc and a velocity range of δv = ±1000 km s−1 around the quasar. In blank fields, we expect to detect only 0.28 CO emitters within the same volume, implying a total overdensity of 17.6 − 7.6 + 11.9 in our fields, and indicating that quasars trace massive structures in the early universe. We quantify this overdensity by measuring the small-scale clustering of CO emitters around quasars, resulting in a cross-correlation length of r 0 , QG = 8.37 − 2.04 + 2.42 h − 1 cMpc, assuming a fixed slope γ = 1.8. This contradicts the reported mild overdensities (x1.4) of Lyα emitters (LAEs) in the same fields at scales of R ≲ 7 h −1 cMpc, which are well described by a cross-correlation length 3.0 − 1.4 + 1.5 times lower than that measured for CO emitters. We discuss some possibilities to explain this discrepancy, including low star formation efficiency, and excess of dust in galaxies around quasars. Finally, we constrain, for the first time, the clustering of CO emitters at z ∼ 4, finding an autocorrelation length of r 0,CO = 3.14 ±1.71 h −1 cMpc (with γ = 1.8). Our work, together with the previous study of LAEs around quasars, traces simultaneously the clustering properties of both optical and dusty galaxy populations in quasars fields, stressing the importance of multiwavelength studies, and highlighting important questions about galaxy properties in high-z dense environments.
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Méndez, José N., Qiang Jin, María González, Wei Hehua, and Cyril D. Boateng. "Predicting and 3D modeling of karst zones using seismic facies analysis in Ordovician carbonates of the Tahe oilfield, China." Interpretation 8, no. 2 (May 1, 2020): T293—T307. http://dx.doi.org/10.1190/int-2019-0090.1.
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Karsted carbonates of the Ordovician Yingshan Formation represent significant hydrocarbon reservoirs in the Tarim Basin, China. Due to the geologic complexity of the formation, realistically predicting and modeling karst zones and rock properties is challenging. This drives the need to apply diverse techniques for building a suitable geologic model. We have developed a static model approach that uses fully automated seismic facies classification processes for predicting and modeling patterns associated with karst elements. Our method uses a seismic attribute and well logs as input data. We initially processed a seismic facies volume using the hierarchical clustering technique. This is based on seismic attribute values that take into account an optimal number of classes. The outcome reveals various patterns illustrated with low amplitudes highlighting the geomorphology of paleokarst elements. Simultaneously, a seismic traces map of the karsted interval was processed using the hybrid clustering technique conducted on seismic trace shape. In this case, the karst facies was extracted from the output and used as secondary input data in trend analysis of the model. Both outputs obtained from clustering techniques are processed in a volume of the most probable facies, which delineate the karst patterns. The results of the modeling process are visualized in various time slices and cross sections, appropriately recognizing the relationship of estimated patterns with karst zones. We have evaluated the karstification thickness and porosity map obtained from the 3D model that detail a reasonable connectivity between karst elements. This is based on the paleogeographic location and type of filling, as well as the dissolution development along the main striking faults. Finally, our method outputs a logical model of karst zones located within the host rock, which reduces the uncertainty and identify nonperforated segments.
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Rosner, Burton S., and Leonard B. Meyer. "The Perceptual Roles of Melodic Process, Contour, and Form." Music Perception 4, no. 1 (1986): 1–39. http://dx.doi.org/10.2307/40285350.
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A melodic process traces a melody's principal motions from its main beginning to its closure. Western tonal music seems built around a handful of different melodic processes. In each of three experiments, we took melodically complete excerpts from fully instrumented recordings of music of the Classical and Romantic periods. The stimuli instantiated either of two different melodic processes. The processes varied across experiments. Subjects judged the dissimilarity of pairs of stimuli. Multidimensional scaling and hierarchical clustering showed that melodic processes and overall properties of contour were important in determining the subjects' perceptions. In a fourth experiment, two-part forms proved perceptually distinguishable from three-part forms. A final experiment which systematically varied both process and form indicated that process and contour can completely mask any perceptual effects of form. On balance, melodic process, contour, and form influence the perception of music in descending order of importance.
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Benabbes, Khalid, Brahim Hmedna, Khalid Housni, Ahmed Zellou, and Ali El Mezouary. "New Automatic Hybrid Approach for Tracking Learner Comprehension Progress in the LMS." International Journal of Interactive Mobile Technologies (iJIM) 16, no. 19 (October 19, 2022): 61–80. http://dx.doi.org/10.3991/ijim.v16i19.33733.
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Learning style is a significant learner-difference factor. Each learner has a preferred learning style and a different way of processing and understanding the novelty. In this paper, a new approach that automatically identify learners learning styles based on their interaction with the Learning Management System (LMS) is introduced. To implement this approach, the traces of 920 enrolled learners in three agronomy courses were exploited using an unsupervised clustering method to group learners according to their degree of engagement. The decision tree classification algorithm relies on the decision rules construction, which is widely adopted to identify the accurate learning style. As missing good decision rules would lead to learning style misclassification, the Felder-Silverman Learning Style Model (FSLSM) is used as it is among the most adopted models in the technology of quality improvement process. The results of this research highlight that most learners prefer the global learning style.
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Thohari, Slamet. "Pandangan Disabilitas dan Aksesibilitas Fasilitas Publik bagi Penyandang Disabilitas di Kota Malang." IJDS Indonesian Journal of Disability Studies 1, no. 1 (July 10, 2014): 27–37. http://dx.doi.org/10.21776/ub.ijds.2014.01.01.04.
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The paper traces how people in Malang percieve people with disabilities, therefore it looks for the base-line of accessibility on public services for people with disabilities as well. It’s nased on a reseach used a quantitaif method in which we use samples based on the standard regulated by goverment. On the other hand, a survey on perception of people with disability in Malang, it used clustering method which is investigating disability issues to people in Malang based on area already pointed out. Our Finding shows that people in Malang beleived that people with disabilities are “unperfect people”, therefe they still beleive on special schools. The other results showed that public services in Malang are not accessible for people with disabilities. The trend data showed that almost of public facilities are categorized as “not accesible”, only some can be categorized as “less-accesibble” and 0% of public facilities are “accessible.
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Cui, Hang, and Tarek Abdelzaher. "SenseLens: An Efficient Social Signal Conditioning System for True Event Detection." ACM Transactions on Sensor Networks 18, no. 2 (May 31, 2022): 1–27. http://dx.doi.org/10.1145/3485047.
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This article narrows the gap between physical sensing systems that measure physical signals and social sensing systems that measure information signals by (i) defining a novel algorithm for extracting information signals (building on results from text embedding) and (ii) showing that it increases the accuracy of truth discovery—the separation of true information from false/manipulated one. The work is applied in the context of separating true and false facts on social media, such as Twitter and Reddit, where users post predominantly short microblogs. The new algorithm decides how to aggregate the signal across words in the microblog for purposes of clustering the miscroblogs in the latent information signal space, where it is easier to separate true and false posts. Although previous literature extensively studied the problem of short text embedding/representation, this article improves previous work in three important respects: (1) Our work constitutes unsupervised truth discovery, requiring no labeled input or prior training. (2) We propose a new distance metric for efficient short text similarity estimation, we call Semantic Subset Matching , that improves our ability to meaningfully cluster microblog posts in the latent information signal space. (3) We introduce an iterative framework that jointly improves miscroblog clustering and truth discovery. The evaluation shows that the approach improves the accuracy of truth-discovery by 6.3%, 2.5%, and 3.8% (constituting a 38.9%, 14.2%, and 18.7% reduction in error, respectively) in three real Twitter data traces.
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Huang, J., M. Deng, Y. Zhang, and H. Liu. "COMPLEX ROAD INTERSECTION MODELLING BASED ON LOW-FREQUENCY GPS TRACK DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 23–28. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-23-2017.
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It is widely accepted that digital map becomes an indispensable guide for human daily traveling. Traditional road network maps are produced in the time-consuming and labour-intensive ways, such as digitizing printed maps and extraction from remote sensing images. At present, a large number of GPS trajectory data collected by floating vehicles makes it a reality to extract high-detailed and up-to-date road network information. Road intersections are often accident-prone areas and very critical to route planning and the connectivity of road networks is mainly determined by the topological geometry of road intersections. <b>A few studies paid attention on detecting complex road intersections and mining the attached traffic information</b> (e.g., connectivity, topology and turning restriction) from massive GPS traces. To the authors’ knowledge, recent studies mainly used high frequency (1&thinsp;s sampling rate) trajectory data to detect the crossroads regions or extract rough intersection models. <b>It is still difficult to make use of low frequency (20&ndash;100&thinsp;s) and easily available trajectory data to modelling complex road intersections geometrically and semantically</b>. The paper thus attempts to construct precise models for complex road intersection by using low frequency GPS traces. We propose to firstly extract the complex road intersections by a LCSS-based (Longest Common Subsequence) trajectory clustering method, then delineate the geometry shapes of complex road intersections by a K-segment principle curve algorithm, and finally infer the traffic constraint rules inside the complex intersections.
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Schoier, Gabriella, and Giuseppe Borruso. "Spatial Data Mining for Highlighting Hotspots in Personal Navigation Routes." International Journal of Data Warehousing and Mining 8, no. 3 (July 2012): 45–61. http://dx.doi.org/10.4018/jdwm.2012070103.
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Rapid developments in the availability and access to spatially referenced information in a variety of areas have induced the need for better analytical techniques to understand the various phenomena. In particular, the authors’ analysis is an insight into a wealth of geographical data collected by individuals as activity dairy data. The attention is drawn on point datasets corresponding to GPS traces driven along a same route in different days. In this paper, the authors explore the presence of clusters along the route, trying to understand the origins and motivations behind that to better understand the road network structure in terms of ’dense’ spaces along the network. Therefore, the attention is focused on methods to highlight such clusters and see their impact on the network structure. Spatial clustering algorithms are examined (DBSCAN) and a comparison with other non-parametric density based algorithm (Kernel Density Estimation) is performed. Different tests are performed over the urban area of Trieste (Italy), considering both multiple users and different origin/destination journeys.
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Hossain, Md Arafat, Jun Han, Jean-Guy Schneider, Jiaojiao Jiang, Muhammad Ashad Kabir, and Steve Versteeg. "Extracting Formats of Service Messages with Varying Payloads." ACM Transactions on Internet Technology 22, no. 3 (August 31, 2022): 1–31. http://dx.doi.org/10.1145/3503159.
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Having precise specifications of service APIs is essential for many Software Engineering activities. Unfortunately, available documentation of services is often inadequate and/or imprecise and, hence, cannot be fully relied upon. Generating service documentation manually is a tedious and error-prone task, especially in light of changes to services. Therefore, there is a need for automated support in generating service documentation. In this work, we present a novel approach to infer the API of a service by analyzing recorded messages sent to and received from this service. Our approach includes a novel, two-level clustering technique to cluster messages, a step that many existing approaches to infer message formats fail to perform precisely in the presence of significant variation of payload information of the available messages. We have evaluated our approach on message traces from four different real-world services. The experimental result shows that our approach is more effective than existing techniques in extracting correct message formats from recorded messages.
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Ali, El mezouary, Hmedna Brahim, and Omar Baz. "An Unsupervised Method for Discovering How Does Learners' Progress Toward Understanding in MOOCs." International Journal of Innovative Technology and Exploring Engineering 10, no. 5 (March 30, 2021): 40–49. http://dx.doi.org/10.35940/ijitee.e8673.0310521.
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Massive Open Online Course (MOOC) seems to expand access to education and it present too many advantages as: democratization of learning, openness to all and accessibility on a large scale, etc. However, this new phenomenon of open learning suffers from the lack of personalization; it is not easy to identify learners’ characteristics because their heterogeneous masse. Following the increasing adoption of learning styles as personalization criteria, it is possible to make learning process easier for learners. In this paper, we extracted features from learners' traces when they interact with the MOOC platform in order to identify learning styles in an automatic way. For this purpose, we adopted the Felder-Silverman Learning Style Model (FSLSM) and used an unsupervised clustering method. Finally, this solution was implemented to clustered learners based on their level of preference for the sequential/global dimension of FSLSM. Results indicated that, first: k-means is the best performing algorithm when it comes to the identification of learning styles; second: the majority of learners show strong and moderate sequential learning style preferences.
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Boutin, Arnaud, and Julien Doyon. "A sleep spindle framework for motor memory consolidation." Philosophical Transactions of the Royal Society B: Biological Sciences 375, no. 1799 (April 6, 2020): 20190232. http://dx.doi.org/10.1098/rstb.2019.0232.
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Sleep spindle activity has repeatedly been found to contribute to brain plasticity and consolidation of both declarative and procedural memories. Here we propose a framework for motor memory consolidation that outlines the essential contribution of the hierarchical and multi-scale periodicity of spindle activity, as well as of the synchronization and interaction of brain oscillations during this sleep-dependent process. We posit that the clustering of sleep spindles in ‘trains', together with the temporally organized alternation between spindles and associated refractory periods, is critical for efficient reprocessing and consolidation of motor memories. We further argue that the long-term retention of procedural memories relies on the synchronized (functional connectivity) local reprocessing of new information across segregated, but inter-connected brain regions that are involved in the initial learning process. Finally, we propose that oscillatory synchrony in the spindle frequency band may reflect the cross-structural reactivation, reorganization and consolidation of motor, and potentially declarative, memory traces within broader subcortical–cortical networks during sleep. This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future'.
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Lekova, Anna. "Exploiting Mobile Ad Hoc Networking and Knowledge Generation to Achieve Ambient Intelligence." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–6. http://dx.doi.org/10.1155/2012/262936.
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Ambient Intelligence (AmI) joins together the fields of ubiquitous computing and communications, context awareness, and intelligent user interfaces. Energy, fault-tolerance, and mobility are newly added dimensions of AmI. Within the context of AmI the concept of mobile ad hoc networks (MANETs) for “anytime and anywhere” is likely to play larger roles in the future in which people are surrounded and supported by small context-aware, cooperative, and nonobtrusive devices that will aid our everyday life. The connection between knowledge generation and communication ad hoc networking is symbiotic—knowledge generation utilizes ad hoc networking to perform their communication needs, and MANETs will utilize the knowledge generation to enhance their network services. The contribution of the present study is a distributed evolving fuzzy modeling framework (EFMF) to observe and categorize relationships and activities in the user and application level and based on that social context to take intelligent decisions about MANETs service management. EFMF employs unsupervised online one-pass fuzzy clustering method to recognize nodes' mobility context from social scenario traces and ubiquitously learn “friends” and “strangers” indirectly and anonymously.
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Cowan, Levi P., and Robert E. Hart. "An Objective Identification and Climatology of Upper-Tropospheric Jets near Atlantic Tropical Cyclones." Monthly Weather Review 148, no. 7 (July 1, 2020): 3015–36. http://dx.doi.org/10.1175/mwr-d-19-0262.1.
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Abstract An objective algorithm is developed for identifying jets in 200-hPa flow and applied to reanalysis data within 2000 km of Atlantic tropical cyclones (TCs) during 1979–2015. The resulting set of 16 512 jets is analyzed both qualitatively and quantitatively to describe the climatology of TC–jet configurations and jet behavior near TCs. Jets occur most commonly poleward of TCs within the 500–1000-km annulus, where TC outflow amplifies the background potential vorticity gradient. A rigorous clustering analysis is performed, resulting in statistically distinct clusters of jet traces that correspond to common configurations of large-scale flow near Atlantic TCs. The speed structure of westerly jets poleward of TCs is found to vary with location in the Atlantic basin, but acceleration of jets downstream of their closest approach to the TC due to interaction with the TC’s diabatic outflow is a consistent feature of these structures. In addition to the climatology developed here, this objectively constructed dataset of upper-tropospheric jets opens unique avenues for exploring TC–environment interactions and utilizing jets to quantitatively describe large-scale flow.
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Zhao, Cheng, Chia-Hsun Chuang, Julian Bautista, Arnaud de Mattia, Anand Raichoor, Ashley J. Ross, Jiamin Hou, et al. "The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: 1000 multi-tracer mock catalogues with redshift evolution and systematics for galaxies and quasars of the final data release." Monthly Notices of the Royal Astronomical Society 503, no. 1 (February 24, 2021): 1149–73. http://dx.doi.org/10.1093/mnras/stab510.
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ABSTRACT We produce 1000 realizations of synthetic clustering catalogues for each type of the tracers used for the baryon acoustic oscillation and redshift space distortion analysis of the Sloan Digital Sky Surveys-iv extended Baryon Oscillation Spectroscopic Survey final data release (eBOSS DR16), covering the redshift range from 0.6 to 2.2, to provide reliable estimates of covariance matrices and test the robustness of the analysis pipeline with respect to observational systematics. By extending the Zel’dovich approximation density field with an effective tracer bias model calibrated with the clustering measurements from the observational data, we accurately reproduce the two- and three-point clustering statistics of the eBOSS DR16 tracers, including their cross-correlations in redshift space with very low computational costs. In addition, we include the gravitational evolution of structures and sample selection biases at different redshifts, as well as various photometric and spectroscopic systematic effects. The agreements on the auto-clustering statistics between the data and mocks are generally within $1\, \sigma$ variances inferred from the mocks, for scales down to a few $h^{-1}\, {\rm Mpc}$ in configuration space, and up to $0.3\, h\, {\rm Mpc}^{-1}$ in Fourier space. For the cross correlations between different tracers, the same level of consistency presents in configuration space, while there are only discrepancies in Fourier space for scales above $0.15\, h\, {\rm Mpc}^{-1}$. The accurate reproduction of the data clustering statistics permits reliable covariances for multi-tracer analysis.
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Alanka, Sandeep, Chanamala Ratnam, and Balla Srinivasa Prasad. "An effective approach to synthesize carbon nanotube-reinforced Al matrix composite precursor." Science and Engineering of Composite Materials 25, no. 5 (September 25, 2018): 983–91. http://dx.doi.org/10.1515/secm-2017-0229.
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AbstractAluminum-based nanocomposites reinforced with carbon nanotubes have increased scientific attention in today’s life. The dispersion quality was the critical aspect, which decides the homogeneous distribution of CNTs within the Al matrix as starting precursors. In this study, a new attempt has been made to obtain a uniformly dispersed Al-0.75% CNT precursor via combining ultra-sonication, cubic tumbler rod milling, and spray drying. This process was integrated with organic deflocculant (formulator) in specific proportion to transform as a semi-wet-based route. The effect of milling media on the morphology and interface structure of the as-produced composite precursor after all the processing steps was investigated through scanning electron microscope (SEM), high-resolution transmission electron microscope (HRTEM), X-ray diffraction (XRD) analysis, Raman spectroscopy, and Fourier transform infrared spectroscopy. The results reveal that the approach is effective in CNT dispersion in Al precursor, which shields the nanotube structure from damage for longer periods of milling time due to the organic formulator mixture, and also, the CNT retention in the Al precursor with minimum clustering is identified compared to the ball milling process. Carbon traces were confirmed in the as-produced composite precursor by this approach.