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Journal articles on the topic 'Collective classification'

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Published: 4 June 2021
Last updated: 13 February 2022

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1

Fan, Shuangfei, and Bert Huang. "Recurrent collective classification." Knowledge and Information Systems 60, no. 2 (August 30, 2018): 741–55. http://dx.doi.org/10.1007/s10115-018-1260-4.

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2

Monner, Derek D., and James A. Reggia. "Recurrent Neural Collective Classification." IEEE Transactions on Neural Networks and Learning Systems 24, no. 12 (December 2013): 1932–43. http://dx.doi.org/10.1109/tnnls.2013.2270376.

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Sen, Prithviraj, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. "Collective Classification in Network Data." AI Magazine 29, no. 3 (September 6, 2008): 93. http://dx.doi.org/10.1609/aimag.v29i3.2157.

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Many real-world applications produce networked data such as the world-wide web (hypertext documents connected via hyperlinks), social networks (for example, people connected by friendship links), communication networks (computers connected via communication links) and biological networks (for example, protein interaction networks). A recent focus in machine learning research has been to extend traditional machine learning classification techniques to classify nodes in such networks. In this article, we provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real-world data.
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Laorden, C., B. Sanz, I. Santos, P. Galan-Garcia, and P. G. Bringas. "Collective classification for spam filtering." Logic Journal of IGPL 21, no. 4 (July 30, 2012): 540–48. http://dx.doi.org/10.1093/jigpal/jzs030.

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Wang, Liping, and Songcan Chen. "Joint representation classification for collective face recognition." Pattern Recognition 63 (March 2017): 182–92. http://dx.doi.org/10.1016/j.patcog.2016.10.004.

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6

Xia, Xin, David Lo, Xinyu Wang, and Xiaohu Yang. "Collective Personalized Change Classification With Multiobjective Search." IEEE Transactions on Reliability 65, no. 4 (December 2016): 1810–29. http://dx.doi.org/10.1109/tr.2016.2588139.

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Xu, Junyi, LE LI, Xin Lu, Shengze Hu, Bin Ge, Weidong Xiao, and Li Yao. "Behavior-Based Collective Classification in Sparsely Labeled Networks." IEEE Access 5 (2017): 12512–25. http://dx.doi.org/10.1109/access.2017.2723433.

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8

Sauvé, Méric, and Ysabel Provencher. "Défense collective des droits sociaux : des modèles d’intervention aux stratégies d’action." Reflets 23, no. 1 (August 3, 2017): 57–81. http://dx.doi.org/10.7202/1040749ar.

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Au début des années 1970, les travaux de Jack Rothman sur les pratiques en organisation communautaire ont introduit la notion de modèle de pratique en intervention communautaire. À sa classification initiale des modèles de la planification sociale, du développement local et de l’action sociale se sont ajoutées, au fil des décennies, les classifications d’autres auteures et auteurs américains et canadiens. Chacun de ces auteures et auteurs a contribué à caractériser davantage les pratiques d’intervention communautaire traditionnellement associées au domaine de l’action sociale. Les connaissances accumulées permettent aujourd’hui de proposer une classification plus fine des pratiques d’action sociale en fonction de trois stratégies : la stratégie émancipatoire, la stratégie de coopération-persuasion et la stratégie de confrontation.
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Uylaş Satı, Nur. "A Collective Learning Approach for Semi-Supervised Data Classification." Pamukkale University Journal of Engineering Sciences 24, no. 5 (2018): 864–69. http://dx.doi.org/10.5505/pajes.2017.44341.

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Krishnapriya, V., and Anuraj Mohan. "Multi-label Collective Classification Using Link Based Label Diffusion." Procedia Computer Science 143 (2018): 157–64. http://dx.doi.org/10.1016/j.procs.2018.10.372.

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Wang, Shaokai, Yunming Ye, Xutao Li, Xiaohui Huang, and Raymond Y. K. Lau. "Semi-supervised Collective Classification in Multi-attribute Network Data." Neural Processing Letters 45, no. 1 (April 1, 2016): 153–72. http://dx.doi.org/10.1007/s11063-016-9517-y.

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Newlands, Gemma, Christoph Lutz, and Christian Fieseler. "Collective action and provider classification in the sharing economy." New Technology, Work and Employment 33, no. 3 (September 6, 2018): 250–67. http://dx.doi.org/10.1111/ntwe.12119.

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Fujii, Keisuke, Takeshi Kawasaki, Yuki Inaba, and Yoshinobu Kawahara. "Prediction and classification in equation-free collective motion dynamics." PLOS Computational Biology 14, no. 11 (November 5, 2018): e1006545. http://dx.doi.org/10.1371/journal.pcbi.1006545.

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Riddell, Audrey P., Stephen A. Fitzgerald, Chu Qi, and Bogdan M. Strimbu. "Classification Strategies for Unbalanced Binary Maps: Finding Ponderosa Pine (Pinus ponderosa) in the Willamette Valley." Remote Sensing 12, no. 20 (October 13, 2020): 3325. http://dx.doi.org/10.3390/rs12203325.

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Forest species classifications are becoming increasingly automated as advances are made in machine learning. Complex algorithms can reach high accuracies, but are not always suitable for small-scale classifications, which may benefit from simpler conventional methods. The goal of this classification was to identify contiguous stands of ponderosa pine (Pinus ponderosa Douglas ex Lawson) against a mix of forest and non-forest background in the southern Willamette Valley, Oregon. The study area is approximately 816,600 ha, considerably larger than most study areas used for presenting techniques for tree species classification. To achieve the objective, we used two classification procedures, one parametric and one non-parametric. For the parametric method, we selected the maximum likelihood (ML) algorithm, whereas for the non-parametric method we chose the random forest (RF) algorithm. To identify ponderosa pine, we used 1 m spatial resolution red-green-blue-infrared (RGBI) aerial images supplied by the U.S. National Agriculture Imagery Program (NAIP) and 1 m spatial resolution canopy height models (CHMs) provided by the Oregon Department of Geology and Mineral Industries (DOGAMI). We tested four data variations for each method: Aerial imagery, CHM-masked aerial imagery, aerial imagery with an additional CHM band, and CHM-masked aerial imagery with a CHM band. The parametric classifications of aerial imagery alone reached an average kappa coefficient of 0.29, which increased to 0.51 when masked with CHM data. The incorporation of CHM data as a fifth band resulted in a similar improvement in kappa (0.47), but the most effective parametric method was the incorporation of CHM data as both a fifth band and a post-classification mask, resulting in a kappa coefficient of 0.89. The non-parametric classification of aerial imagery achieved a mean validation kappa coefficient of 0.85 collectively and 0.90 individually, which only increased by approximately 0.01 or less when the CHM masks were applied. The addition of the CHM band increased the kappa value to 0.91 for both individual and collective tile classifications. The highest kappa of all methods was achieved through five-band non-parametric classification with the addition of the CHM band (0.94) for both collective and individual classifications. Our results suggest that parametric methods, when enhanced with a CHM mask, could be suitable for large-area, small-scale classifications based on RGBI imagery, but a non-parametric classification of fused spectral and height data will generally achieve the highest accuracy for large, unbalanced datasets.
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Pronskikh, Vitaly S. "Ontology of the Collective Experimentalist." Epistemology & Philosophy of Science 56, no. 4 (2019): 165–82. http://dx.doi.org/10.5840/eps201956474.

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In this article, the collective experimenter, arising in scientific projects from those modeled on the Alvarez group to megascience, is studied in the framework of the model of trading zones, as well as Actor-Network Theory. The collective experimenter is defined as a network of actors whose forms are trading zones, including the core – the empirical collective subject of cognition – and the peripheral part. The multitude of actors of the collective experimenter includes the core, as well as the community of intentions and the external actors that are part of the periphery of the collective experimenter. Attention is focused on the differences between the author of epistemic claims, the subject of cognition and scientific collaboration. A classification of collective experimentalists is proposed that includes four types of ontologies. The classification is applied to JINR scientific projects, and within its framework projects of the Alvarez type, big science, proto-megascience and megascience are distinguished. Ways of developing projects to the megascience-level through the formation of cores-communicative communities in the structure of the collective experimenter are proposed. Premised on the results obtained, recommendations are formulated for the development of the JINR experiments program.
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Gao, Wenliang, Nobuhiro Kaji, Naoki Yoshinaga, and Masaru Kitsuregawa. "Collective Sentiment Classification Based on User Leniency and Product Popularity." Journal of Natural Language Processing 21, no. 3 (2014): 541–61. http://dx.doi.org/10.5715/jnlp.21.541.

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Wu, Qingyao, Yunming Ye, Shen-Shyang Ho, and Shuigeng Zhou. "Semi-supervised multi-label collective classification ensemble for functional genomics." BMC Genomics 15, Suppl 9 (2014): S17. http://dx.doi.org/10.1186/1471-2164-15-s9-s17.

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18

Wu, Qingyao, Michael K. Ng, Yunming Ye, Xutao Li, Ruichao Shi, and Yan Li. "Multi-label collective classification via Markov chain based learning method." Knowledge-Based Systems 63 (June 2014): 1–14. http://dx.doi.org/10.1016/j.knosys.2014.02.012.

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19

YOON, Jisung, Jinhyuk YUN*, and Woo-Sung JUNG*. "Build Up of a Subject Classification System from Collective Intelligence." New Physics: Sae Mulli 68, no. 6 (June 29, 2018): 647–54. http://dx.doi.org/10.3938/npsm.68.647.

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20

van Zomeren, Martijn, and Russell Spears. "Metaphors of Protest: A Classification of Motivations for Collective Action." Journal of Social Issues 65, no. 4 (December 2009): 661–79. http://dx.doi.org/10.1111/j.1540-4560.2009.01619.x.

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21

Jeawak, Shelan S., Christopher B. Jones, and Steven Schockaert. "Predicting the environment from social media: A collective classification approach." Computers, Environment and Urban Systems 82 (July 2020): 101487. http://dx.doi.org/10.1016/j.compenvurbsys.2020.101487.

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22

Tian, Yonghong, Tiejun Huang, and Wen Gao. "Latent linkage semantic kernels for collective classification of link data." Journal of Intelligent Information Systems 26, no. 3 (May 2006): 269–301. http://dx.doi.org/10.1007/s10844-006-2208-9.

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23

ZHAO, Pengya, Xiangling FU, Weiqiang WU, Da LI, and Songfeng GAO. "Collective classification method based on label propagation for fraud detection." Journal of Shenzhen University Science and Engineering 37, no. 5 (September 1, 2020): 482–89. http://dx.doi.org/10.3724/sp.j.1249.2020.05482.

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24

Kitikidou, Kyriaki, Elias Milios, and Panagiota Palavouzi. "Development of an ensemble classifier with data from description sheets to classify forest stands in site qualities." Šumarski list 141, no. 3-4 (April 30, 2017): 131–37. http://dx.doi.org/10.31298/sl.141.3-4.3.

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Aim of study: In this work, we tested the technique of combining the predictions of classifiers for the development of a single, ensemble classifier, in order to classify forest stands in site qualities. Area of study: We used data of the forest stands of Dadia – Lefkimi – Soufli forest (north-eastern Greece). Materials and methods: The variables that we used as input were the altitude, slope, age and canopy density. For the ensemble classifier development we applied the boosting algorithm. Main results: The canopy density was the most important predictor; topography which replaced altitudes and slopes was the second important predictor, while the developed ensemble classifier gave a percentage of correct classification up to 98.59% (for the worst site quality). Research essentials: If we consider that the initial site classification comprised over 70% of the Dadia-Lefkimi –Soufli forest area in the worst site quality, then the usage of boosting method for creating a collective classifier for site qualities in the studied forest can be characterized as fully successful. The application of this method using these input parameters do not need background information regarding the tree age and (or) other difficult to access information. Moreover, in a quite high degree, this site classification is not influenced by disturbances. The boosting method for creating a collective classifier for site qualities obviously will give far more accurate classifications of site productivity, if a more sophisticated scheme of data collection is used.
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Shedd, Louis, Stephen Katsinas, and Nathaniel Bray. "Unionization and Monetary Compensation at America’s Access Institutions: Assessing the Impact of Frames That Do or Do Not Consider Geography." Educational Policy 32, no. 2 (November 22, 2017): 255–79. http://dx.doi.org/10.1177/0895904817741466.

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This article categorizes institutions under both the 2015 Carnegie Basic Classification system and the mission-driven classification system, and further analyzes both by the presence of a collective bargaining agreement. The goal of this article was to use the presentation of data on revenue, employment numbers, salary outlays, and the presence or lack of collective bargaining agreements to identify any differences between the new “Mission-Driven” classification system in direct comparison with the 2015 Carnegie Basic Classification system in recognizing the results of unionization on monetary compensation for higher education employees.
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Tkatchenko, Alexandre, and Nikola Batina. "Classification of hexagonal adlayer arrangements by means of collective geometrical properties." Journal of Chemical Physics 125, no. 16 (October 28, 2006): 164702. http://dx.doi.org/10.1063/1.2360530.

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27

Bagnall, Anthony, Jason Lines, Jon Hills, and Aaron Bostrom. "Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles." IEEE Transactions on Knowledge and Data Engineering 27, no. 9 (September 1, 2015): 2522–35. http://dx.doi.org/10.1109/tkde.2015.2416723.

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28

Castriota, Stefano, and Marco Delmastro. "Seller Reputation: Individual, Collective, and Institutional Factors." Journal of Wine Economics 7, no. 1 (May 2012): 49–69. http://dx.doi.org/10.1017/jwe.2012.4.

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AbstractIn this paper, we study firm reputation by investigating the interaction between individual reputations of Italian wineries and a large set of (possible) determinants. With respect to winery reputation, we find a positive effect for firm age and size and producer's intrinsic motivations and a negative effect for outsourcing, while horizontal differentiation, ownership status, and hiring well-known consultants play no role. Further, collective reputation and institutional regulation exert a significantly positive effect on individual reputation. (JEL Classification: L14, L15)
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Wu, Qingyao, Mingkui Tan, Xutao Li, Huaqing Min, and Ning Sun. "NMFE-SSCC: Non-negative matrix factorization ensemble for semi-supervised collective classification." Knowledge-Based Systems 89 (November 2015): 160–72. http://dx.doi.org/10.1016/j.knosys.2015.06.026.

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Tang, Song, Mao Ye, Qihe Liu, and Fan Li. "Domain adaptation of image classification based on collective target nearest-neighbor representation." Journal of Electronic Imaging 25, no. 3 (May 27, 2016): 033006. http://dx.doi.org/10.1117/1.jei.25.3.033006.

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Winkler, Julia K., Katharina Sies, Christine Fink, Ferdinand Toberer, Alexander Enk, Mohamed Souhayel Abassi, Tobias Fuchs, et al. "Collective human intelligence outperforms artificial intelligence in a skin lesion classification task." JDDG: Journal der Deutschen Dermatologischen Gesellschaft 19, no. 8 (June 7, 2021): 1178–84. http://dx.doi.org/10.1111/ddg.14510.

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Smalii, O. B. "A general description of threats to european security at the threshold of the 21st century." Legal horizons, no. 21 (2020): 148–52. http://dx.doi.org/10.21272/legalhorizons.2020.i21.p148.

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Over time, the issue of security has not lost its relevance, but on the contrary gained momentum because of the large-scale threats that have arisen and evolve. In today's world, there are threats that are beyond the control of a particular country and, for the sake of security, begin to pool their forces and resources. The author reveals the particularities of threats to European collective security in the 21st century. The article identifies threats based on doctrinal approaches. The threat is a set of factors and conditions that could potentially pose a danger to the state in the future. The author examines approaches to the classification of threats to collective security and their characteristics, as well as the author's classification of threats according to their relevance and time of occurrence. According to scientific approaches, the threats to European collective security are divided into: economic, military, information, environmental and other nature; direct and indirect; potential and immediate; external and internal. The author proposed his own classification of threats, according to which they are divided into: new threats, again relevant and classic. The author shows the threats influence on the formation of the European collective security system and how they affect its current changes. It is noted that contemporary external threats were emerging at the turn of the late 1980s – early 1990s, and some of them continue to be shaped and strengthened today. Given the current challenges that pose a threat to collective security, a number of specific actions need to be taken to improve the performance of collective security organizations in order to more effectively address contemporary threats. The author proposes ways to improve the activity of modern collective security systems in order to avoid the emergence and development of large-scale threats to collective security in the 21st century.
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van Waterschoot, Walter, and Christophe Van den Bulte. "The 4P Classification of the Marketing Mix Revisited." Journal of Marketing 56, no. 4 (October 1992): 83–93. http://dx.doi.org/10.1177/002224299205600407.

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McCarthy's 4P classification of the marketing mix instruments has received wide acceptance in past decades. In recent years, however, increasing criticism has been voiced, among other reasons because of its inherent negative definition of sales promotion and its lack of mutual exclusiveness and collective exhaustiveness. The authors evaluate the 4P classification against the criteria proposed by Hunt and present an improved classification.
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Stetsiuk, B. O. "Types of musical improvisation: a classification discourse." Problems of Interaction Between Arts, Pedagogy and the Theory and Practice of Education 57, no. 57 (March 10, 2020): 178–96. http://dx.doi.org/10.34064/khnum1-57.11.

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This article systemizes the types of musical improvisation according to various approaches to this phenomenon. It uses as the basis the classification by Ernst Ferand, which presently needs to be supplemented and clarified. It was stressed that the most general approach to the phenomenon of musical improvisation is its classification based on the layer principle (folklore, academic music, “third” layer). Within these layers, there are various forms of musical improvisation whose systemization is based on different principles, including: performer composition (collective or solo improvisation), process technology (full or partial improvisation), thematic orientation (improvisation theme in a broad and narrow context), etc. It was emphasized that classification of musical improvisation by types is manifested the most vividly when exemplified by jazz, which sums up the development of its principles and forms that shaped up in the previous eras in various regions of the world and have synthetized in the jazz language, which today reflects the interaction between such fundamental origins of musical thought as improvisation and composition. It was stated that the basic principles for classification of the types of musical improvisation include: 1) means of improvisation (voices; keyboard, string, wind and percussion instruments); 2) performer composition (solo or collective improvisation); 3) textural coordinates (vertical, horizontal, and melodic or harmonic improvisation, respectively); 4) performance technique (melodic ornaments, coloring, diminutiving, joining voices in the form of descant, organum, counterpoint); 5) scale of improvisation (absolute, relative; total, partial); 6) forms of improvisation: free, related; ornamental improvisation, variation, ostinato, improvisation on cantus firmus or another preset material (Ernst Ferand). It was stressed that as of today, the Ferand classification proposed back in 1938 needs to be supplemented by a number of new points, including: 1) improvisation of a mixed morphological type (music combined with dance and verbal text in two versions: a) invariable text and dance rhythm, b) a text and dance moves that are also improvised); 2) “pure” musical improvisation: vocal, instrumental, mixed (S. Maltsev). The collective form was the genetically initial form of improvisation, which included all components of syncretic action and functioned within the framework of cult ritual. Only later did the musical component per se grow separated (autonomous), becoming self-sufficient but retaining the key principle of dialogue that helps reproduce the “question-answer” system in any types of improvisation – a system that serves as the basis for creation of forms in the process of improvisation. Two more types of improvisation occur on this basis, differing from each other by communication type (Y. Lotman): 1) improvisation “for oneself” (internal type, characterized by reclusiveness and certain limitedness of information); 2) improvisation “for others” (external type, characterized by informational openness and variegation). It was emphasized that solo improvisation represents a special variety of musical improvisation, which beginning from the Late Renaissance era becomes dominating in the academic layer, distinguishable in the initial phase of its development for an improvising writing dualism (M. Saponov). The classification criterion of “composition” attains a new meaning in the system of professional music playing, to which improvisation also belongs. Its interpretation becomes dual and applies to the performance and textural components of improvisation, respectively. With regard to the former, two types occur in the collective form of improvisation: 1) improvisation by all participants (simultaneous or consecutive); 2)improvisation by a soloist against the background of invariable fixed accompaniment in other layers of music performance. The following types of improvisation occur in connection with the other – textural – interpretation of the term “composition”, which means inner logical principle of organization of musical fabric (T. Bershadska): 1) monodic, or monophonic (all cases of solo improvisation by voice or on melodic wind instruments); 2) heterophonic (collective improvisation based on interval duplications and variations of the main melody); 3) polyphonic (different-picture melodies in party voices of collective improvisation); 4) homophonic-harmonic (a combination of melodic and harmonic improvisations, typical for the playing on many-voiced harmonic instruments). It was emphasized that in the theory of musical improvisation, there is a special view at texture: on the one hand, it (like in a composition) “configures” (E. Nazaikinskyi) the musical fabric, and on the other hand, it is not a final representation thereof, i.e., it does not reach the value of Latin facio (“what has been done”). A work of improvisation is not an amorphous musical fabric; on the contrary, it contains its own textural organization, which, unlike a written composition, is distinguishable for the mobility and variability of possible textural solutions. The article’s concluding remarks state that classification of the types of musical improvisation in the aspect of its content and form must accommodate the following criteria: 1) performance type (voices, instruments, performance method, composition of participants, performance location); 2) texture type (real acoustic organization of musical space in terms of vertical, horizontal and depth parameters); 3) thematic (in the broad and narrow meanings of this notion: from improvisation on “idea theme” or “image theme” to variation improvisations on “text theme”, which could be represented by various acoustic structures: modes, ostinato figures of various types, melody themes like jazz evergreens, harmonic sequences, etc.).
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Butail, Sachit, Erik M. Bollt, and Maurizio Porfiri. "Analysis and classification of collective behavior using generative modeling and nonlinear manifold learning." Journal of Theoretical Biology 336 (November 2013): 185–99. http://dx.doi.org/10.1016/j.jtbi.2013.07.029.

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Cavalcante, Marília Daniella Machado Araújo, Liliana Müller Larocca, Maria Marta Nolasco Chaves, Márcia Regina Cubas, Laura Christina Macedo Piosiadlo, and Verônica de Azevedo Mazza. "Nursing terminology as a work process instrument of nurses in collective health." Revista da Escola de Enfermagem da USP 50, no. 4 (August 2016): 610–16. http://dx.doi.org/10.1590/s0080-623420160000500010.

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Abtract OBJECTIVE To analyze the use of nursing terminology as an instrument of the nursing work process in Collective Health. METHOD Exploratory case study. For data collection was conducted a group interview with 24 nurses working in health units of a municipality in south central Paraná, Brazil. Data were analyzed in the light of interdependence between the structural, particular and singular dimensions contained in the Theory of Nursing Praxis Intervention in Collective Health. RESULTS The situations interfering with improper use were the lack of knowledge about the origin and purpose of terminology, lack of training, and non-mandatory use. CONCLUSION Although the nursing terminology is used as an instrument in the nursing work process in collective health, it requires training to be recognized as a classification system. At the same time, institutional policies should be employed to ensure the effective use of these instruments.
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Carrothers, A. W. R. "A Way of Thinking about Collective Bargaining: Circumstance, Policy, Law and Actuality." Articles 40, no. 2 (April 12, 2005): 351–66. http://dx.doi.org/10.7202/050138ar.

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The object of this paper is to offer a taxonomy — a kind of classification System — as an aid to thinking, in a number of interrelated dimensions, about collective bargaining as a phenomenon of the relationship between management and labour.
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KOVBATIUK, М. V., and V. O. SHEVCHUK. "CONCEPTUAL APPROACHES TO PROPERTY CLASSIFICATION: POLITICAL - ECONOMICAL ASPECT." Economic innovations 21, no. 1(70) (March 20, 2019): 75–84. http://dx.doi.org/10.31520/ei.2019.21.1(70).75-84.

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Topicality. Property is a difficult complex system category, which in its development underwent significant continuous quantitative and qualitative changes. The problem of different interpretations of the economic category �property� aggravated by the fact that scientists lack a unanimous approach to identifying types, kinds and forms of ownership, which indicates the diversity of approaches to the classification of property. Aim and tasks. of the article is to substantiate the theoretical approaches to the definition of the economic content of property in the process of its transformation and development and the study of the classification of types and forms of ownership in particular from the point of viev of traditional and modern classification. Research results. The article analyzes the approaches of outstanding scientists to the theoretical concept of the category "property" in different eras of the development of economic science. The study showed that at present it is advisable to distinguish between classical and modern classification of property. Therefore, the authors have formed a classical classification that includes two types of property: private, which exists in two forms: simple private property and capitalistic private property, and public property, which has two types � collective and state property. The modern classification based on modern views and trends in the evolution of property relations in society, based on the position that it is incorrect to single out only private and public types of ownership, because in this approach a separate individual and a work collective are identified, which may include tens, hundreds or more workers. Conclusions. Therefore, in the modern classification of property, types of ownership are collective (group), private, which are combined into a group of non-state types of ownership, as well as public (state) and supranational which form a group of state ownership types. In spite of the fact that there are no species in the modern classification of property and in the classification according to the legislation, practical experience indicates that there are still varieties of property. Therefore, it is advisable to classify property not only by types and forms, but divided into types according to various classification criteria, namely by objects of ownership, by subjects of ownership, depending on the type of property, by the number of owners, by features of objects, by levels of privatization.
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Karabagias, Karabagias, and Badeka. "The Honey Volatile Code: A Collective Study and Extended Version." Foods 8, no. 10 (October 17, 2019): 508. http://dx.doi.org/10.3390/foods8100508.

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Background: The present study comprises the second part of a new theory related to honey authentication based on the implementation of the honey code and the use of chemometrics. Methods: One hundred and fifty-one honey samples of seven different botanical origins (chestnut, citrus, clover, eucalyptus, fir, pine, and thyme) and from five different countries (Egypt, Greece, Morocco, Portugal, and Spain) were subjected to analysis of mass spectrometry (GC-MS) in combination with headspace solid-phase microextraction (HS-SPME). Results: Results showed that 94 volatile compounds were identified and then semi-quantified. The most dominant classes of compounds were acids, alcohols, aldehydes, esters, ethers, phenolic volatiles, terpenoids, norisoprenoids, and hydrocarbons. The application of classification and dimension reduction statistical techniques to semi-quantified data of volatiles showed that honey samples could be distinguished effectively according to both botanical origin and the honey code (p < 0.05), with the use of hexanoic acid ethyl ester, heptanoic acid ethyl ester, octanoic acid ethyl ester, nonanoic acid ethyl ester, decanoic acid ethyl ester, dodecanoic acid ethyl ester, tetradecanoic acid ethyl ester, hexadecanoic acid ethyl ester, octanal, nonanal, decanal, lilac aldehyde C (isomer III), lilac aldehyde D (isomer IV), benzeneacetaldehyde, alpha-isophorone, 4-ketoisophorone, 2-hydroxyisophorone, geranyl acetone, 6-methyl-5-hepten-2-one, 1-(2-furanyl)-ethanone, octanol, decanol, nonanoic acid, pentanoic acid, 5-methyl-2-phenyl-hexenal, benzeneacetonitrile, nonane, and 5-methyl-4-nonene. Conclusions: New amendments in honey authentication and data handling procedures based on hierarchical classification strategies (HCSs) are exhaustively documented in the present study, supporting and flourishing the state of the art.
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Kiranyaz, Serkan, Turker Ince, Stefan Uhlmann, and Moncef Gabbouj. "Collective Network of Binary Classifier Framework for Polarimetric SAR Image Classification: An Evolutionary Approach." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42, no. 4 (August 2012): 1169–86. http://dx.doi.org/10.1109/tsmcb.2012.2187891.

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Chołoniewski, J., J. Sienkiewicz, J. Hołyst, and M. Thelwall. "The Role of Emotional Variables in the Classification and Prediction of Collective Social Dynamics." Acta Physica Polonica A 127, no. 3a (March 2015): A—21—A—28. http://dx.doi.org/10.12693/aphyspola.127.a-21.

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Uchino, Eiichiro, Kanata Suzuki, Noriaki Sato, Ryosuke Kojima, Yoshinori Tamada, Shusuke Hiragi, Hideki Yokoi, et al. "Classification of glomerular pathological findings using deep learning and nephrologist–AI collective intelligence approach." International Journal of Medical Informatics 141 (September 2020): 104231. http://dx.doi.org/10.1016/j.ijmedinf.2020.104231.

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43

Wu, Qingyao, Jian Chen, Shen-Shyang Ho, Xutao Li, Huaqing Min, and Chao Han. "Multi-Label Regularized Generative Model for Semi-Supervised Collective Classification in Large-Scale Networks." Big Data Research 2, no. 4 (December 2015): 187–201. http://dx.doi.org/10.1016/j.bdr.2015.04.002.

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DeDeo, Simon, David Krakauer, and Jessica Flack. "Evidence of strategic periodicities in collective conflict dynamics." Journal of The Royal Society Interface 8, no. 62 (February 16, 2011): 1260–73. http://dx.doi.org/10.1098/rsif.2010.0687.

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We analyse the timescales of conflict decision-making in a primate society. We present evidence for multiple, periodic timescales associated with social decision-making and behavioural patterns. We demonstrate the existence of periodicities that are not directly coupled to environmental cycles or known ultraridian mechanisms. Among specific biological and socially defined demographic classes, periodicities span timescales between hours and days. Our results indicate that these periodicities are not driven by exogenous or internal regularities but are instead driven by strategic responses to social interaction patterns. Analyses also reveal that a class of individuals, playing a critical functional role, policing, have a signature timescale of the order of 1 h. We propose a classification of behavioural timescales analogous to those of the nervous system, with high frequency, or α -scale, behaviour occurring on hour-long scales, through to multi-hour, or β -scale, behaviour, and, finally γ periodicities observed on a timescale of days.
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Titus, Mathew, George Hagstrom, and James R. Watson. "Unsupervised manifold learning of collective behavior." PLOS Computational Biology 17, no. 2 (February 12, 2021): e1007811. http://dx.doi.org/10.1371/journal.pcbi.1007811.

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Collective behavior is an emergent property of numerous complex systems, from financial markets to cancer cells to predator-prey ecological systems. Characterizing modes of collective behavior is often done through human observation, training generative models, or other supervised learning techniques. Each of these cases requires knowledge of and a method for characterizing the macro-state(s) of the system. This presents a challenge for studying novel systems where there may be little prior knowledge. Here, we present a new unsupervised method of detecting emergent behavior in complex systems, and discerning between distinct collective behaviors. We require only metrics, d(1), d(2), defined on the set of agents, X, which measure agents’ nearness in variables of interest. We apply the method of diffusion maps to the systems (X, d(i)) to recover efficient embeddings of their interaction networks. Comparing these geometries, we formulate a measure of similarity between two networks, called the map alignment statistic (MAS). A large MAS is evidence that the two networks are codetermined in some fashion, indicating an emergent relationship between the metrics d(1) and d(2). Additionally, the form of the macro-scale organization is encoded in the covariances among the two sets of diffusion map components. Using these covariances we discern between different modes of collective behavior in a data-driven, unsupervised manner. This method is demonstrated on a synthetic flocking model as well as empirical fish schooling data. We show that our state classification subdivides the known behaviors of the school in a meaningful manner, leading to a finer description of the system’s behavior.
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Nechaeva, A. A. "Theoretical and Methodological Approaches to Collective Memory Analysis in Memory Studies." Discourse 6, no. 3 (July 20, 2020): 46–63. http://dx.doi.org/10.32603/2412-8562-2020-6-3-46-63.

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Introduction. Collective memory research helps to uncover the deeply imbedded meaning of the past in the present, to follow the identity development process in various communities, to find narrative structures that define societal foundations. Simultaneously, such research can be complicated from the empirical point of view. The scientific novelty of the given article lies in the fact that theoretical and methodological approaches to collective memory research have not yet been summarized and systematized up to this date. The goal of this research is to provide such a review and determine the most valid research methods in Memory Studies. The relevance of the presented research is determined by the fact that the proposition of a methodological apparatus for Memory Studies is necessary for the finalization of its formation as an independent discipline.Methodology and sources. Collective memory theory served as the theoretical-methodological foundation of the conducted research, it allowed to view the past not as a set given but as an object undergoing interpretation and representation. Such academics as M. Halbwachs, M. Bloch, A. Warburg, Jan and Aleida Assmann, J. Olick, A. Erll and others developed the following theory. A range of scientists dedicated their work to the discovery of collective memory research methods, among them M. Bulanova, W. Kansteiner, A. Erll, B. Zelizer, A. Confino, T. Kapitonova, V. Belokrylova, etc. J. Olick made a considerable impact into the understanding of the given issue, having suggested to view memory as a process developing in time, which required to define the methods of analysis that would be able to take this characteristic into account. However, a complete list as well as a general system and classification of methods have not been developed in the academic literature up to the present day. Having appeared at an intersection of various humanities and social sciences, Memory Studies adopts empirical research methods from Sociology, Political science, Culture Studies, Psychology, Media Studies, Visual Studies, etc. In course of the presented research, the relevant empirical research works in the Memory Studies field by international and Russian authors have been analyzed, we considered the research carried out by Ch. Lindt, A. Vasil'ev, T. Emel'yanova, A. Timofeeva, V. Kasamara, E. Hakokongas, E. Keightley, M. Meyers, B. Zelizer, and others. That allowed us to determine the most frequently applied collective memory research methods, to compile their overview and develop the author classification of the used methods.Results and discussion. An overview of key theoretical approaches to collective memory research was provided. They include functional, phenomenological, post-structural, social-historical and information approaches. J. Olick enriched the list of five theoretical approaches suggested by M. Bulanova by introducing the process-relativist approach to studying collective memory. The main research methods applied in Memory Studies were outlined; moreover, a classification of key disciplinary traditions that academics turn to in memory research was introduced featuring sociological, psychological, information, cultural and historical traditions as well as a separate branch of Computer Sciences.Conclusion. As a result of the conducted research, a systematic overview and an author's classification of theoretical-methodological approaches to collective memory analysis were introduced. The findings of the given research can be implemented by a range of academics working on the issues of group identity building, ways of working with contested past, historical events representation in the present, the functioning of memory communities, etc. The defining of the methodological apparatus of Memory studies serves as a moving force for the effective development, generalization and bringing to a common understanding the further research of collective memory structures formation and distribution as well as concepts related to it.
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Lundin, Katarina. "Att instruera i idrottskontext." Educare - vetenskapliga skrifter, no. 1 (April 6, 2018): 140–69. http://dx.doi.org/10.24834/educare.2018.1.7.

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This article investigates language use in sports, and more specific how trainers address their young trainees in athletics, jujutsu, and gymnastics. The article focuses the instructions that the trainees receive when performing different exercises during training. The primary theoretical framework is Bernstein’s (1971 and onwards) concepts classification, framing, and a collective as opposed to an integrated code, concepts which are otherwise used in other contexts. Against this theoretical background, the trainers’ language use is analyzed. In brief, the results show that the trainers’ language for instructions can be described as a collective code, due to a strong classification and a strong framing. This has impact on and at the same time is the result of the trainers’ language use.
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Долинская, Владимира, and Vladimira Dolinskaya. "Organizational-Legal Forms of Economical Activity: System and New Regulation." Journal of Russian Law 4, no. 1 (January 25, 2016): 0. http://dx.doi.org/10.12737/17227.

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The presence of collective and of individual and collective types of economical activity. The collective economical activity is based on contractual and charter principles. The definition of organizational-legal form of economical activity has been suggested. The expansion of organizational and legal forms of economical activity has been described. The comparative description of an individual person and legal entity have been given. The legal personality of a legal entity has been considered in terms of applied additional requirements — optional as well as special ones. The revealed meaning of legal classifications of legal entities shows that the efficiency of economical activity management does not depend on the mentioned classification. 2 types of economic activity of legal entities have been identified: business activity — commercial legal entities and income generating activities — non-commercial legal entities. The property basis of non-commercial entities engaged into profit generating activity was taken into consideration; it’s been noted that the size of such activity evolves special requirements. The main problems of legal control over profit generating activity have been formulated. The “commercialization” of the legal entities activities has been proved. The need of legalization of other legal personalities in addition to juridical persons has been justified.
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Wouters, Mathias. "The classification of employment relationships in Belgium." European Labour Law Journal 10, no. 3 (September 2019): 198–218. http://dx.doi.org/10.1177/2031952519864196.

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This chapter provides an overview of the characteristics of the Belgian employment contract and, in particular, of the concept of ‘subordination’. After having painted a picture of what differentiates an employment contract from a contract for services, it, subsequently, assesses the classification of certain specific examples, such as self-employed persons with only one client. The role of economic dependency in the classification of working relationships is discussed by using these examples. The chapter furthermore emphasises the strong binary divide between employment and self-employment. It goes into more detail on the country’s collective bargaining mechanisms for false and genuine self-employed workers. After having described the Belgian take on identifying the ‘employing entity’, the chapter finishes off by outlining the contemporary debate on the reform of the Belgian classification mechanisms.
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ROEHNER, B., and D. SORNETTE. "ANALYSIS OF THE PHENOMENON OF SPECULATIVE TRADING IN ONE OF ITS BASIC MANIFESTATIONS: POSTAGE STAMP BUBBLES." International Journal of Modern Physics C 10, no. 06 (September 1999): 1099–116. http://dx.doi.org/10.1142/s0129183199000905.

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We document and analyze the empirical facts concerning one of the clearest evidence of speculation in financial trading as observed in the postage collection stamp market. We unravel some of the mechanisms of speculative behavior which emphasize the role of fancy and collective behavior. In our conclusion, we propose a classification of speculative markets based on two parameters, namely the amplitude of the price peak and a second parameter that measures its "sharpness". This study is offered to anchor modeling efforts to realistic market constraints and observations.
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