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RANDALL, D. J., H. J. HAMILTON, and R. J. HILDERMAN. "TEMPORAL GENERALIZATION WITH DOMAIN GENERALIZATION GRAPHS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 02 (March 1999): 195–217. http://dx.doi.org/10.1142/s0218001499000124.
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This paper addresses the problem of using domain generalization graphs to generalize temporal data extracted from relational databases. A domain generalization graph associated with an attribute defines a partial order which represents a set of generalization relations for the attribute. We propose formal specifications for domain generalization graphs associated with calendar (date and time) attributes. These graphs are reusable (i.e. can be used to generalize any calendar attributes), adaptable (i.e. can be extended or restricted as appropriate for particular applications), and transportable (i.e. can be used with any database containing a calendar attribute).
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Nguyen, Ha, and Wendy V. Wismer. "Temporal Sensory Profiles of Regular and Sodium-Reduced Foods Elicited by Temporal Dominance of Sensations (TDS) and Temporal Check-All-That-Apply (TCATA)." Foods 11, no. 3 (February 3, 2022): 457. http://dx.doi.org/10.3390/foods11030457.
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Temporal sensory methods can be used to highlight the impact of sodium reduction on the dynamic sensory profile of foods targeted for sodium reduction. Study aims were to compare the temporal sensory attribute profiles of regular and sodium-reduced food products elicited by TDS and TCATA, over single and multiple oral intakes. A total of 20 semi-trained participants evaluated commercially available regular and sodium-reduced canned corn, cooked ham (single intakes), potato chips and cream of mushroom soup (5 intakes) using both TDS and TCATA. Regular and sodium-reduced products differed in not only salty but also other sensory attributes, noticeably dry for chips, sweet for corn, bitter and metallic for ham, thick, creamy, sweet, and starchy for soup. TDS and TCATA provided comparable information for the key sensory attributes characterizing and differentiating the regular and sodium-reduced products. TDS profiled significant differences between samples for a larger number of attributes than TCATA, while TCATA profiles were more consistent across intakes. Multiple intakes changed the duration of attribute dominance but not the number of significantly dominant attributes in TDS profiles. The current findings provide insight for applications of temporal profiling to other food products and development of sodium-reduced foods with attribute profiles acceptable to consumers.
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Chalyi, Serhii, and Ievgen Bogatov. "METHOD OF CONSTRUCTING AN ATTRIBUTE DESCRIPTION OF THE BUSINESS PROCESS "AS IS" IN THE PROCESS APPROACH TO ENTERPRISE MANAGEMENT." EUREKA: Physics and Engineering 6 (November 30, 2018): 35–40. http://dx.doi.org/10.21303/2461-4262.2018.00786.
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The problem of constructing an attribute description of a business process with the automated formation of process models “as is” using logs of information systems in which the tracks of individual processes are not identified is considered. It is shown that to solve this problem, it is advisable to distinguish the distinctive properties of individual business processes represented by the attributes of log events. A method for constructing an attribute description of a business process is proposed. The method is based on the comparison of combinations of attributes for intervals of events of a fixed length and the subsequent selection of subsets of attributes with the same values. The method includes the steps of forming the intervals of events, constructing combinations of attributes for specified intervals, as well as calculating and subsequently averaging the weights of combinations of attributes on these intervals. The result of the method is a weight-ordered set of event attributes and their values, which takes into account the attribute and temporal aspects of the business process. The method creates conditions for a more efficient transition from functional to process management based on splitting the log into processes using the resulting attribute description and subsequent prototyping of business process models “as is” by means of process mining.
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Fujisaki, Waka, and Shin'ya Nishida. "Sensory Attribute Identification Time Cannot Explain the Common Temporal Limit of Binding Different Attributes and Modalities." i-Perception 2, no. 8 (October 2011): 758. http://dx.doi.org/10.1068/ic758.
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Wu, Bo, Xun Liang, Xiangping Zheng, and Jun Wang. "Enhancing Dynamic GCN for Node Attribute Forecasting with Meta Spatial-Temporal Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 2023): 16360–61. http://dx.doi.org/10.1609/aaai.v37i13.27040.
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Node attribute forecasting has recently attracted considerable attention. Recent attempts have thus far utilize dynamic graph convolutional network (GCN) to predict future node attributes. However, few prior works have notice that the complex spatial and temporal interaction between nodes, which will hamper the performance of dynamic GCN. In this paper, we propose a new dynamic GCN model named meta-DGCN, leveraging meta spatial-temporal tasks to enhance the ability of dynamic GCN for better capturing node attributes in the future. Experiments show that meta-DGCN effectively modeling comprehensive spatio-temporal correlations between nodes and outperforms state-of-the-art baselines on various real-world datasets.
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Fan, Wenfei, Resul Tugay, Yaoshu Wang, Min Xie, and Muhammad Asif Ali. "Learning and Deducing Temporal Orders." Proceedings of the VLDB Endowment 16, no. 8 (April 2023): 1944–57. http://dx.doi.org/10.14778/3594512.3594524.
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This paper studies how to determine temporal orders on attribute values in a set of tuples that pertain to the same entity, in the absence of complete timestamps. We propose a creator-critic framework to learn and deduce temporal orders by combining deep learning and rule-based deduction, referred to as GATE (Get the lATEst). The creator of GATE trains a ranking model via deep learning, to learn temporal orders and rank attribute values based on correlations among the attributes. The critic then validates the temporal orders learned and deduces more ranked pairs by chasing the data with currency constraints; it also provides augmented training data as feedback for the creator to improve the ranking in the next round. The process proceeds until the temporal order obtained becomes stable. Using real-life and synthetic datasets, we show that GATE is able to determine temporal orders with F -measure above 80%, improving deep learning by 7.8% and rule-based methods by 34.4%.
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Colloc, Joël, Relwendé Aristide Yameogo, Peter Summons, Lilian Loubet, Jean-Bernard Cavelier, and Paul Bridier. "A Temporal Case-Based Reasoning Platform Relying on a Fuzzy Vector Spaces Object-Oriented Model and a Method to Design Knowledge Bases and Decision Support Systems in Multiple Domains." Algorithms 15, no. 2 (February 19, 2022): 66. http://dx.doi.org/10.3390/a15020066.
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Knowledge bases in complex domains must take into account many attributes describing numerous objects that are themselves components of complex objects. Temporal case-based reasoning (TCBR) requires comparing the structural evolution of component objects and their states (attribute values) at different levels of granularity. This paper provides some significant contributions to computer science. It extends a fuzzy vector space object-oriented model and method (FVSOOMM) to present a new platform and a method guideline capable of designing objects and attributes that represent timepoint knowledge objects. It shows how temporal case-based reasoning can use distances between temporal fuzzy vector functions to compare these knowledge objects’ evolution. It describes examples of interfaces that have been implemented on this new platform. These include an expert’s interface that describes a knowledge class diagram; a practitioner’s interface that instantiates domain objects and their attribute constraints; and an end-user interface to input attribute values of the real cases stored in a domain case database. This paper illustrates resultant knowledge bases in different domains, with examples of pulmonary embolism diagnosis in medicine and decision making in French municipal territorial recomposition. The paper concludes with the current limitations of the proposed model, its future perspectives and possible platform enhancements.
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Newton, Peter F. "Developmental Trends of Black Spruce Fibre Attributes in Maturing Plantations." International Journal of Forestry Research 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/7895289.
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This study assessed the temporal developmental patterns of commercially relevant fibre attributes (tracheid length and diameters, wall thickness, specific surface area, wood density, microfibril angle, fibre coarseness, and modulus of elasticity) and their interrelationships within maturing black spruce (Picea mariana (Mill.) B.S.P.) plantations. A size-based stratified random sample procedure within 5 semimature plantations located in the Canadian Boreal Forest Region was used to select 50 trees from which radial cross-sectional xylem sequences at breast-height (1.3 m) were cut and analyzed. Statistically, the graphical and linear correlation analyses indicated that the attributes exhibited significant (p≤0.05) relationships among themselves and with morphological tree characteristics. Relative variation of each annually measured attribute declined with increasing size class (basal area quintile). The transitional shifts in temporal correlation patterns occurring at the time of approximate crown closure where suggestive of intrinsic differences in juvenile and mature wood formation processes. The temporal cumulative development patterns of all 8 of the annually measured attributes varied systematically with tree size and exhibited the most rapid rates of change before the trees reached a cambial age of 20 years. At approximately 50 years after establishment, plantation mean attribute values were not dissimilar from those reported for more mature natural-origin stands.
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Altuna, Begoña, María Jesús Aranzabe, and Arantza Díaz de Ilarraza. "EusTimeML: A mark-up language for temporal information in Basque." Research in Corpus Linguistics 8 (2020): 86–104. http://dx.doi.org/10.32714/ricl.08.01.06.
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We present EusTimeML, a mark-up language for temporal information in texts written in Basque. It is compliant with the TimeML specifications, while offering some adapted attributes and attribute values in order to represent the language-specific features of Basque. In particular, alterations have been carried out for verb tense, aspect and modality coding, as well as for time expression and signal annotation. EusTimeML also provides a major extension to the existing TimeML schemes, since the attributes and values for factuality annotation have been added to the existing temporal information annotation scheme. EusTimeML has been used to annotate the EusTimeBank Corpus, the news and history narratives corpus that has been used as the gold standard in temporal information processing in Basque.
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Liu, Yaqin, Yunsi Chen, Qing He, and Qian Yu. "Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective." Systems 11, no. 2 (February 5, 2023): 82. http://dx.doi.org/10.3390/systems11020082.
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With the cyclical development of emerging technologies, in reality, the evolution dynamics of their innovation networks will inevitably show obvious time attributes. Numerous network analyses of real complex systems usually focus on static networks; however, it is difficult to describe that most real networks undergo topological evolutions over time. Temporal networks, which incorporate time attributes into traditional static network models, can more accurately depict the temporal features of network evolution. Here, we introduced the time attribute of the life cycle of emerging technology into the evolution dynamics of its innovation network, constructed an emerging technology temporal innovation network from a temporal network perspective, and established its evolution model in combination with the life cycle and key attributes of emerging technology. Based on this model, we took 5G technology as an example to conduct network evolution simulation, verified the rationality of the above model building, and analyzed the cyclical evolution dynamics of this network in various topological structures. The results show that the life cycle of emerging technology, as well as multiple knowledge attributes based on the key attributes of emerging technology, are important factors that affect network evolution by acting on node behaviors. Within this study, we provide a more realistic framework to describe the internal mechanism of the cyclical evolution of emerging technology innovation network, which can extend the research on innovation network evolution from the single topological dynamics to the topological–temporal dynamics containing time attributes and enrich the research dimensions of innovation network evolution from the perspective of temporal evolution.
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Triska, Jan, and Vilem Vychodil. "Logic of temporal attribute implications." Annals of Mathematics and Artificial Intelligence 79, no. 4 (October 1, 2016): 307–35. http://dx.doi.org/10.1007/s10472-016-9526-6.
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Liu, Jing, and Yong Zhong. "Time-Weighted Community Search Based on Interest." Applied Sciences 12, no. 14 (July 13, 2022): 7077. http://dx.doi.org/10.3390/app12147077.
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Community search aims to provide users with personalized community query services. It is a prerequisite for various recommendation systems and has received widespread attention from academia and industry. The existing literature has established various community search models and algorithms from different dimensions of social networks. Unfortunately, they only judge the representative attributes of users according to the frequency of attribute keywords, completely ignoring the temporal characteristics of keywords. It is clear that a user’s interest changes over time, so it is essential to select users’ representative attributes in combination with time. Therefore, we propose a time-weighted community search model (TWC) based on user interests which fully considers the impact of time on user interests. TWC reduces the number of query parameters as much as possible and improves the usability of the model. We design the time-weighted decay function of the attribute. We then extract the user’s time-weighted representative attributes to express the user’s short-term interests more clearly in the query window. In addition, we propose a new attribute similarity scoring function and a community scoring function. To solve the TWC problem, we design and implement the Local Extend algorithm and the Shrink algorithm. Finally, we conduct extensive experiments on a real dataset to verify the superiority of the TWC model and the efficiency of the proposed algorithm.
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Kesner, Raymond P., and Michael R. Hunsaker. "The temporal attributes of episodic memory." Behavioural Brain Research 215, no. 2 (December 2010): 299–309. http://dx.doi.org/10.1016/j.bbr.2009.12.029.
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Triska, Jan, and Vilem Vychodil. "Minimal bases of temporal attribute implications." Annals of Mathematics and Artificial Intelligence 83, no. 1 (March 6, 2018): 73–97. http://dx.doi.org/10.1007/s10472-018-9576-z.
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Di Monaco, Rossella, Nicoletta Antonella Miele, Stefania Volpe, Paolo Masi, and Silvana Cavella. "Temporal dominance of sensations and dynamic liking evaluation of polenta sticks." British Food Journal 118, no. 3 (March 7, 2016): 749–60. http://dx.doi.org/10.1108/bfj-07-2015-0236.
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Purpose – Temporal dominance of sensation (TDS) is a sensory method developed by Pineau et al. (2003) which studies the sequence of dominant sensations of a product during its consumption. TDS is believed to be more appropriate to explain consumer responses than static descriptive analysis due to its temporal element. The purpose of this paper is to define the temporal sensory profile of a new product: polenta stick. In particular, TDS method was used to measure the dominance of sensory attributes in polenta stick samples and dynamic consumer tests were performed in order to verify if the acceptability changed over time during sample consumption. Design/methodology/approach – Eight polenta sticks, different in terms of storage conditions, cooking procedures and serving temperatures, were analysed by means of TDS with 13 assessors. During two preliminary sessions, the attributes list, constituted by the nine most cited sensations, was generated. Five replications were carried out. In dynamic consumer tests, 50 subjects were asked to give their liking on a seven-category scale for the frozen samples, in different five moments during the evaluation. Findings – TDS results showed a significant effect of the experimental conditions on dominant attribute perception of polenta sticks. For the oven-cooked samples, more flavour attributes were perceived as dominant, whereas for the fried samples, the attributes crispness and oiliness overcame with a high panel dominance rate and for a long time. For the chilled samples, crispness had the highest panel dominance rate; whereas for the frozen samples, creaminess was the most dominant attribute. Consumer liking scores did not significantly change over time during consumption for all the samples. The fried samples received the highest liking scores, at both serving temperatures. Practical implications – The chosen sensory methods gave the authors important information about the real perception of the products during consumption. A lot of foodstuffs show several sensory properties that appear in different times during evaluation and/or consumption. These properties could affect overall liking so they should be taken into account. Originality/value – New dynamic sensory methods were used to characterize a new food product, i.e. polenta-based sticks. The procedure used to evaluate the liking by consumers was completely innovative, whereas the sensory method used to characterize the samples was recently developed. The new food product was developed as an aim of an Italian research project funded by MiSE (Ministero dello Sviluppo Economico) for the valorization of maize flour (MAISFOOD, Industria 2015).
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Ratan Murty, N. Apurva, and S. P. Arun. "Multiplicative mixing of object identity and image attributes in single inferior temporal neurons." Proceedings of the National Academy of Sciences 115, no. 14 (March 20, 2018): E3276—E3285. http://dx.doi.org/10.1073/pnas.1714287115.
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Object recognition is challenging because the same object can produce vastly different images, mixing signals related to its identity with signals due to its image attributes, such as size, position, rotation, etc. Previous studies have shown that both signals are present in high-level visual areas, but precisely how they are combined has remained unclear. One possibility is that neurons might encode identity and attribute signals multiplicatively so that each can be efficiently decoded without interference from the other. Here, we show that, in high-level visual cortex, responses of single neurons can be explained better as a product rather than a sum of tuning for object identity and tuning for image attributes. This subtle effect in single neurons produced substantially better population decoding of object identity and image attributes in the neural population as a whole. This property was absent both in low-level vision models and in deep neural networks. It was also unique to invariances: when tested with two-part objects, neural responses were explained better as a sum than as a product of part tuning. Taken together, our results indicate that signals requiring separate decoding, such as object identity and image attributes, are combined multiplicatively in IT neurons, whereas signals that require integration (such as parts in an object) are combined additively.
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Poom, Leo. "Visual Inter-Attribute Contour Completion." Perception 30, no. 7 (July 2001): 855–65. http://dx.doi.org/10.1068/p3222.
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A new visual phenomenon, inter-attribute illusory (completed) contours, is demonstrated. Contour completions are perceived between any combination of spatially separate pairs of inducing elements (Kanizsa-like ‘pacman’ figures) defined either by pictorial cues (luminance contrast or offset gratings), temporal contrast (motion, second-order-motion or ‘phantom’ contours), or binocular-disparity contrast. In a first experiment, observers reported the perceived occurrence of contour completion for all pair combinations of inducing elements. In a second experiment they rated the perceived clarity of the completed contours. Both methods generated similar results—contour completions were perceived even though the inducing elements were defined by different attributes. Ratings of inter-attribute clarity were no lower than in either of the two corresponding intra-attribute conditions and seem to be the average of these two ratings. The results provide evidence for the existence of attribute-invariant Gestalt processes, and on a mechanistic level indicate that the completion process operates on attribute-invariant contour detectors.
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Wen, Minwei, Hongyan Mei, Wei Wang, and Xing Zhang. "Enhanced Temporal Knowledge Graph Completion via Learning High-Order Connectivity and Attribute Information." Applied Sciences 13, no. 22 (November 16, 2023): 12392. http://dx.doi.org/10.3390/app132212392.
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Temporal knowledge graph completion (TKGC) refers to the prediction and filling in of missing facts on time series, which is essential for many downstream applications. However, many existing TKGC methods suffer from two limitations: (1) they only consider direct relations between entities and fail to express high-order structural dependencies between entities; and (2) they only leverage relation quadruples of temporal knowledge graphs, ignoring attribute information that contains rich semantic information. This makes them vulnerable to sparsity and incompleteness problems. In response, we propose HCAE, a temporal knowledge graph completion model that includes high-order connectivity and attribute information. This consists mainly of a recursive embedding propagation layer and a multi-head attention aggregation layer. The former leverages a recursive mechanism to update entity embeddings and can learn high-order connectivity information between entities in linear complexity time. The latter leverages an attention mechanism to understand the importance of different attributes for entity representation automatically. Combining high-order connectivity and attribute information can lead to more diverse entity representations and help enhance the model’s ability to infer unknown entities. Comparative experiments on three real-world datasets show that the model’s inference accuracy significantly outperforms other benchmark methods, especially regarding knowledge graphs with many unknown entities or relations.
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Batista-Silva, Valéria Flávia, Daiane Dias Boneto, Dayani Bailly, Milza Celi Fedatto Abelha, and Elaine Antoniassi Luiz Kashiwaqui. "Invertebrates associated to Eichhornea azurea Kunth in a lagoon of the Upper Paraná River: composition, community attributes and influence of abiotic factors." Acta Limnologica Brasiliensia 23, no. 4 (June 14, 2012): 376–85. http://dx.doi.org/10.1590/s2179-975x2012005000016.
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AIM: We evaluated the composition and community attributes of invertebrates associated to Eichhornia azurea at Cascalho Lagoon, Upper Paraná River, Mato Grosso do Sul State, Brazil, over a hydrological cycle, as well the possible influence of abiotic factors upon these attributes. METHODS: The samplings were conducted during 2010 in the rainy and dry periods at stands of E. azurea. The attributes evaluated were abundance, richness, diversity, evenness and dominance. The abiotic factors, temperature, dissolved oxygen, pH and turbidity were summarized by a Principal Component Analysis (PCA). In order to verify possible differences between the mean values of the community attributes and the scores of the PCA axis in different periods, we employed null models analysis of variance. The influence of abiotic factors on each attribute was evaluated through Pearson correlations. RESULTS: We captured 3,052 individuals, distributed into 32 taxa, belonging to the phyllum Mollusca, Annelida, Nematoda and Arthropoda. Among the assessed attributes, only abundance and richness varied significantly between periods, with higher values during the rainy period. Chironomidade was dominant in both periods, whereas Notonectidae and Cyclopoida were rare in the rainy, and Bivalve, Decapoda, Haliplidae, Trichoptera and Pyralidae, in the dry period. A temporal distinction was evident only for the PCA axis 1, which represented gradients in temperature, dissolved oxygen and pH. Among the community attributes, only abundance was significant and negatively correlated with this axis. CONCLUSION: We attested that: i) the rainy period should add favorable conditions for invertebrates' higher richness and abundance in this macrophyte; ii) only the later attribute was influenced by limnological gradients.
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Lei, X., W. Song, R. Fan, R. Feng, and L. Wang. "LAND SUBSIDENCE PREDICTION THROUGH MODELING OF TEMPORAL ATTRIBUTE PREDICTION OF KNOWLEDGE GRAPH." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2022 (May 18, 2022): 9–15. http://dx.doi.org/10.5194/isprs-annals-v-4-2022-9-2022.
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Abstract. Land subsidence is a geological disaster. It will lead to the decline of land elevation, resulting in the potential safety hazards of urban facilities. Thus, the prediction of land subsidence displacement is significant. Among the existing prediction methods, the methods based on the time-series prediction model only analyze the settlement series data without considering the settlement mechanism, so they are easy to apply. However, they less consider the influence of other factors on land subsidence. Besides, they independently input displacement time-series data from different monitoring points without considering their relationship. To solve these problems, we take the monitoring point as the entity and take the DEM, soil type, building height, and land subsidence displacement sequence at the corresponding position of the monitoring point as the attributes to construct the knowledge graph. And then, we propose a framework Graph-TAP for modeling temporal attribute prediction of the knowledge graph’s entity. This framework learns the representation of events with the specific entity at first. Then it captures the temporal dependency between historical events using GRU. Finally, it predicts the entity’s displacement attribute. We randomly selected 61 subsidence monitoring points in Shenzhen, China. We used the land subsidence displacement InSAR time-series data (12-day time interval) and other attribute data from June 22, 2015, to April 5, 2016, for model training, validation, and testing. The experimental results show that our method is better than the time-series prediction based on the LSTM model and the DARNET (a knowledge graph temporal attribute prediction framework).
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Gallo, Bruna, José Demattê, Rodnei Rizzo, José Safanelli, Wanderson Mendes, Igo Lepsch, Marcus Sato, Danilo Romero, and Marilusa Lacerda. "Multi-Temporal Satellite Images on Topsoil Attribute Quantification and the Relationship with Soil Classes and Geology." Remote Sensing 10, no. 10 (October 1, 2018): 1571. http://dx.doi.org/10.3390/rs10101571.
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The mapping of soil attributes provides support to agricultural planning and land use monitoring, which consequently aids the improvement of soil quality and food production. Landsat 5 Thematic Mapper (TM) images are often used to estimate a given soil attribute (i.e., clay), but have the potential to model many other attributes, providing input for soil mapping applications. In this paper, we aim to evaluate a Bare Soil Composite Image (BSCI) from the state of São Paulo, Brazil, calculated from a multi-temporal dataset, and study its relationship with topsoil properties, such as soil class and geology. The method presented detects bare soil in satellite images in a time series of 16 years, based on Landsat 5 TM observations. The compilation derived a BSCI for the agricultural sites (242,000 hectare area) characterized by very complex geology. Soil properties were analyzed to calibrate prediction models using 740 soil samples (0–20 cm) collected of the area. Partial least squares regression (PLSR) based on the BSCI spectral dataset was performed to quantify soil attributes. The method identified that a single image represents 7 to 20% of bare soil while the compilation of the multi-temporal dataset increases to 53%. Clay content had the best soil attribute prediction estimates (R2 = 0.75, root mean square error (RMSE) = 89.84 g kg−1, and accuracy = 74%). Soil organic matter, cation exchange capacity and sandy soils also achieved moderate predictions. The BSCI demonstrates a strong relationship with legacy geological maps detecting variations in soils. From a single composite image, it was possible to use spectroscopy to evaluate several environmental parameters. This technique could greatly improve soil mapping and consequently aid several applications, such as land use planning, environmental monitoring, and prevention of land degradation, updating legacy surveys and digital soil mapping.
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Ruhl, Katharine C., and Mark F. Seeman. "The Temporal and Social Implications of Ohio Hopewell Copper Ear Spool Design." American Antiquity 63, no. 4 (October 1998): 651–62. http://dx.doi.org/10.2307/2694113.
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In a recent synthesis of style theory, Carr (1995) proposed a model based on attribute hierarchies, which reveal social patterns. The present study applies Carr's model of style to a large sample of bicymbal copper ear spools, a diagnostic “Hopewell” artifact class of the Middle Woodland period (ca. 150 B.C.—A.D. 400) in eastern North America. After ear spool attributes are defined and ranked for their visibility, a seriation of the ear spools is developed and tested for time and space correlations. Results are consistent with the interpretation that the size of social groups participating in ritual events increased over time, while the technical requirements for ear spool durability decreased. The “visibility” of ear spool attributes relates to patterns of group interaction at the level of the site, sub-region and region, thus supporting Carr's model. The model has limitations when bridging from archaeological data to ethnographic interpretations, although our results indicate that exchange of finished goods and technology was less important within the context of the Hopewell Interaction Sphere than the construction of Hopewell ideologies.
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Lee, Geonu, Kimin Yun, and Jungchan Cho. "Occluded Pedestrian-Attribute Recognition for Video Sensors Using Group Sparsity." Sensors 22, no. 17 (September 1, 2022): 6626. http://dx.doi.org/10.3390/s22176626.
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Pedestrians are often obstructed by other objects or people in real-world vision sensors. These obstacles make pedestrian-attribute recognition (PAR) difficult; hence, occlusion processing for visual sensing is a key issue in PAR. To address this problem, we first formulate the identification of non-occluded frames as temporal attention based on the sparsity of a crowded video. In other words, a model for PAR is guided to prevent paying attention to the occluded frame. However, we deduced that this approach cannot include a correlation between attributes when occlusion occurs. For example, “boots” and “shoe color” cannot be recognized simultaneously when the foot is invisible. To address the uncorrelated attention issue, we propose a novel temporal-attention module based on group sparsity. Group sparsity is applied across attention weights in correlated attributes. Accordingly, physically-adjacent pedestrian attributes are grouped, and the attention weights of a group are forced to focus on the same frames. Experimental results indicate that the proposed method achieved 1.18% and 6.21% higher F1-scores than the advanced baseline method on the occlusion samples in DukeMTMC-VideoReID and MARS video-based PAR datasets, respectively.
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Tseng, Chien-Hao, Chia-Chien Hsieh, Dah-Jing Jwo, Jyh-Horng Wu, Ruey-Kai Sheu, and Lun-Chi Chen. "Person Retrieval in Video Surveillance Using Deep Learning–Based Instance Segmentation." Journal of Sensors 2021 (August 21, 2021): 1–12. http://dx.doi.org/10.1155/2021/9566628.
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Video surveillance systems are deployed at many places such as airports, train stations, and malls for security and monitoring purposes. However, it is laborious to search for and retrieve persons in multicamera surveillance systems, especially with cluttered backgrounds and appearance variations among multiple cameras. To solve these problems, this paper proposes a person retrieval method that extracts the attributes of a masked image using an instance segmentation module for each object of interest. It uses attributes such as color and type of clothes to describe a person. The proposed person retrieval system involves four steps: (1) using the YOLACT++ model to perform pixelwise person segmentation, (2) conducting appearance-based attribute feature extraction using a multiple convolutional neural network classifier, (3) employing a search engine with a fundamental attribute matching approach, and (4) implementing a video summarization technique to produce a temporal abstraction of retrieved objects. Experimental results show that the proposed retrieval system can achieve effective retrieval performance and provide a quick overview of retrieved content for multicamera surveillance systems.
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Maity, Debotyam, and Fred Aminzadeh. "Novel fracture zone identifier attribute using geophysical and well log data for unconventional reservoirs." Interpretation 3, no. 3 (August 1, 2015): T155—T167. http://dx.doi.org/10.1190/int-2015-0003.1.
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We have characterized a promising geothermal prospect using an integrated approach involving microseismic monitoring data, well logs, and 3D surface seismic data. We have used seismic as well as microseismic data along with well logs to better predict the reservoir properties to try and analyze the reservoir for improved mapping of natural and induced fractures. We used microseismic-derived velocity models for geomechanical modeling and combined these geomechanical attributes with seismic and log-derived attributes for improved fracture characterization of an unconventional reservoir. We have developed a workflow to integrate these data to generate rock property estimates and identification of fracture zones within the reservoir. Specifically, we introduce a new “meta-attribute” that we call the hybrid-fracture zone-identifier attribute (FZI). The FZI makes use of elastic properties derived from microseismic as well as log-derived properties within an artificial neural network framework. Temporal analysis of microseismic data can help us understand the changes in the elastic properties with reservoir development. We demonstrate the value of using passive seismic data as a fracture zone identification tool despite issues with data quality.
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Seyedy, Mahmoud, and Ehsan Kordi. "Critical Review of the Mu'tazilī Theory of the Creation (Ḥudūth)of the Qur’an in Qāḍī Abd al-Jabbār’s Opinion, Regarding Imam Reza's Narrations." International Journal of Multicultural and Multireligious Understanding 6, no. 4 (September 3, 2019): 328. http://dx.doi.org/10.18415/ijmmu.v6i4.985.
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The issue of creation/origin (Ḥudūth) or pre-eternity (Qidam) of the Qur’an was one of the oldest theological debates in Muslim world which has been discussed under the title of the word (Kalām or speech) attribute of God. Qāḍī Abd al-Jabbār, one the most important scholars of the Mu’tazilī school, gives several reasons to prove the origin and creation of the Qur’an. Referring to the hadiths (narrations) of Imam Reza (AS), his arguments lead to the temporal origin and gradual descent of the words and sounds of the Qur'an. However, since this is related to the word of God, the arguments of Abdul Jabbār require temporal origin, composition and change in the Divine nature, according to His Word attribute. For, the attribute could not be separated from the attributed and the sentence of creation or pre-eternity pass to each other, as well. The creation of the Qur’an and confining the Divine word in words and sounds provide you with basic challenges, which may not be accepted easily.
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Das, Badhan Chandra, Md Musfique Anwar, Md Al-Amin Bhuiyan, Iqbal H. Sarker, Salem A. Alyami, and Mohammad Ali Moni. "Attribute Driven Temporal Active Online Community Search." IEEE Access 9 (2021): 93976–89. http://dx.doi.org/10.1109/access.2021.3093368.
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Bartels, A., and S. Zeki. "The temporal order of binding visual attributes." Vision Research 46, no. 14 (July 2006): 2280–86. http://dx.doi.org/10.1016/j.visres.2005.11.017.
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Böhlen, Michael, Johann Gamper, and Christian S. Jensen. "An algebraic framework for temporal attribute characteristics." Annals of Mathematics and Artificial Intelligence 46, no. 3 (March 2006): 349–74. http://dx.doi.org/10.1007/s10472-006-9022-5.
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Kim, Kowoon, and Hong-Youl Ha. "Do Changes in Attribute Weights between Two Platforms Alter Interplay Effects in the O2O Era? Two Time-Lag Intervals in the Tourism Sector." Sustainability 15, no. 14 (July 10, 2023): 10789. http://dx.doi.org/10.3390/su151410789.
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Hotels and their online agencies have an interplay effect on the online-to-offline (O2O) platform. Since the new platform cannot do business with one service provider alone, understanding the changes in attributes that determine service satisfaction during subsequent consumption events is crucial. Despite such interrelationships between online agency services and offline hotel services, there remains a dearth of empirical research on this issue. Grounded in the consumption system approach, the present paper attempted to empirically examine the temporal, carryover, and interplay relationships among service attributes, customer satisfaction, and loyalty intentions in the hotel O2O commerce context. We developed a conceptual model for the establishment of consumption systems that consist of two different service providers and tested it using two time-lag interval data. The results showed that the relationship between attribute-level ratings and satisfaction is dynamic for both tourism websites and hotels. In particular, we found that changes in online tourism website attributes are more dynamic than changes in hotel attributes. Furthermore, while the tourism website satisfaction–hotel loyalty intentions linkage strengthens during consumption episodes, the hotel satisfaction–tourism website loyalty intentions linkage weakens, demonstrating the difference in temporal effects on O2O businesses. Meanwhile, although the relationship between satisfaction and loyalty intentions are strengthened in the online tourism sector, the same relationship if weakened in the hotel sector. Finally, we found no carryover effects in the Chinese hospitality sectors.
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Victor, Jonathan D., and Keith P. Purpura. "Spatial Phase and the Temporal Structure of the Response to Gratings in V1." Journal of Neurophysiology 80, no. 2 (August 1, 1998): 554–71. http://dx.doi.org/10.1152/jn.1998.80.2.554.
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Victor, Jonathan D. and Keith P. Purpura. Spatial phase and the temporal structure of the response to gratings in V1. J. Neurophysiol. 80: 554–571, 1998. We recorded single-unit activity of 25 units in the parafoveal representation of macaque V1 to transient appearance of sinusoidal gratings. Gratings were systematically varied in spatial phase and in one or two of the following: contrast, spatial frequency, and orientation. Individual responses were compared based on spike counts, and also according to metrics sensitive to spike timing. For each metric, the extent of stimulus-dependent clustering of individual responses was assessed via the transmitted information, H. In nearly all data sets, stimulus-dependent clustering was maximal for metrics sensitive to the temporal pattern of spikes, typically with a precision of 25–50 ms. To focus on the interaction of spatial phase with other stimulus attributes, each data set was analyzed in two ways. In the “pooled phases” approach, the phase of the stimulus was ignored in the assessment of clustering, to yield an index H pooled. In the “individual phases” approach, clustering was calculated separately for each spatial phase and then averaged across spatial phases to yield an index H indiv. H pooled expresses the extent to which a spike train represents contrast, spatial frequency, or orientation in a manner which is not confounded by spatial phase (phase-independent representation), whereas H indiv expresses the extent to which a spike train represents one of these attributes, provided spatial phase is fixed (phase-dependent representation). Here, representation means that a stimulus attribute has a reproducible and systematic influence on individual responses, not a neural mechanism for decoding this influence. During the initial 100 ms of the response, contrast was represented in a phase-dependent manner by simple cells but primarily in a phase-independent manner by complex cells. As the response evolved, simple cell responses acquired phase-independent contrast information, whereas complex cells acquired phase-dependent contrast information. Simple cells represented orientation and spatial frequency in a primarily phase-dependent manner, but also they contained some phase-independent information in their initial response segment. Complex cells showed primarily phase-independent representation of orientation but primarily phase-dependent representation of spatial frequency. Joint representation of two attributes (contrast and spatial frequency, contrast and orientation, spatial frequency and orientation) was primarily phase dependent for simple cells, and primarily phase independent for complex cells. In simple and complex cells, the variability in the number of spikes elicited on each response was substantially greater than the expectations of a Poisson process. Although some of this variation could be attributed to the dependence of the response on the spatial phase of the grating, variability was still markedly greater than Poisson when the contribution of spatial phase to response variance was removed.
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Libak, Audun, Behzad Alaei, and Anita Torabi. "Fault visualization and identification in fault seismic attribute volumes: Implications for fault geometric characterization." Interpretation 5, no. 2 (May 31, 2017): B1—B16. http://dx.doi.org/10.1190/int-2016-0152.1.
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Fault seismic attribute volumes (such as volumetric coherence and curvature) represent an efficient and objective way to visualize and identify faults in seismic cubes. Fault geometric attributes such as length, height, and fault segmentation can be extracted from such fault seismic attribute volumes. We evaluate the strengths and pitfalls of using coherence volumes for characterization of fault geometry. The results are obtained using a database from the Barents Sea, which contains 35 3D seismic cubes, together with conceptual synthetic seismic models. A high signal-to-noise ratio is a requirement for the extraction of accurate fault geometric data. Noise attenuation methods improve fault visualization, but our results indicate that the effect of noise attenuation on the extracted fault geometric attributes is only clear in areas of low signal-to-noise ratios. The choice of coherence algorithm is important when extracting fault length data. Semblance-based coherence performs better than gradient structure tensor-based coherence in low-displacement areas near the fault tips, and it produces more accurate fault length data. Faults can appear segmented in coherence volumes if relatively similar reflectors are juxtaposed across a fault. In such areas, it is important that the interpreter does not overlook the fault. The size of the analysis window used in coherence calculations controls the resolution and continuity of the imaged faults. Our results support an optimal temporal window size of one to two times the dominant period of the seismic data (typically 7–17 samples in conventional 4 ms sampled 3D seismic data). Larger temporal window sizes can result in an overestimation of fault height, especially for small faults. A large spatial window can smear out segmentation along the fault and make the fault traces wider. Even though a large spatial window can have some positive effects, we recommend using a relatively small spatial window (five traces) when extracting subtle fault geometric attributes.
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Natsume, Hiroharu, Shogo Okamoto, and Hikaru Nagano. "TDS Similarity: Outlier Analysis Using a Similarity Index to Compare Time-Series Responses of Temporal Dominance of Sensations Tasks." Foods 12, no. 10 (May 17, 2023): 2025. http://dx.doi.org/10.3390/foods12102025.
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Temporal dominance of sensations (TDS) methods are used to record temporally developing sensations while eating food samples. Results of TDS tasks are typically discussed using averages across multiple trials and panels, and few methods have been developed to analyze differences between individual trials. We defined a similarity index between two time-series responses of TDS tasks. This index adopts a dynamic level to determine the importance of the timing of attribute selection. With a small dynamic level, the index focuses on the duration for attributes to be selected rather than on the timing of the attribute selection. With a large dynamic level, the index focuses on the temporal similarity between two TDS tasks. We performed an outlier analysis based on the developed similarity index using the results of TDS tasks performed in an earlier study. Certain samples were categorized as outliers irrespective of the dynamic level, whereas the categorization of a few samples depended on the level. The similarity index developed in this study achieved individual analyses of TDS tasks, including outlier detection, and adds new analysis techniques to TDS methods.
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Chen, Wen Qiang, Guo Qiang Xiao, and Xiao Qin Tang. "An Action Recognition Model Based on the Bayesian Networks." Applied Mechanics and Materials 513-517 (February 2014): 1886–89. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1886.
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In this paper, we propose a novel human action recognition method based on Bayesian network model and high-level semantic concept (human action attribute). Firstly, we extract 3D-SIFT descriptors for each spatio-temporal interest point in the videos. Secondly, the bag of words is used as the low-level feature to represent these videos. Finally, Bayesian networks related to action attributes are trained based on MAP (Maximum A Posterior Probability) to recognize human behavior. The experimental results show that the proposed model is effective on action recognition.
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Zhang, Wei, Tao Zhang, Zhengbo Fu, Ping Ai, Guoqing Yao, and Jianwei Qi. "Emerging Spatio-temporal Hot Spot Analysis of Beijing Subsidence Trend Detection Based on PS-InSAR." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1-2024 (May 11, 2024): 861–66. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-2024-861-2024.
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Abstract. Scholars have done a lot of research on urban settlement, but it is difficult to give consideration to the temporal and spatial attributes of settlement at the same time in its display and analysis. Most of them focused on the analysis of regional settlement, single point settlement curve and settlement rate map at a certain time, but few combined time and space for collaborative analysis. Therefore, in this paper, 32 scenes Sentinel-1B SAR data are used to obtain settlement data of Beijing via PS-InSAR method. Secondly, combined with the temporal and spatial attributes of settlement results, the subsidence law revealed by using spatio-temporal cube slicing and attribute filtering. Finally, subsidence development trend and the detection of abnormal subsidence are explored by emerging hot spots (ESH) analysis. The experimental results show that the settlement funnel center in Beijing is mainly concentrated near the junction of Chaoyang district and Tongzhou district. The settlement range tends to expand. There are several local continuous subsidence areas in the settlement oscillating area. Spatio-temporal analysis makes the development trend of urban settlement more intuitive. Emerging hotspot analysis combined with Getis-Ord Gi* statistics and Mann-Kendall trend test could more effectively analyze the settlement trend of the study area and detect new potential settlement centers, so that to provide auxiliary decision-making for urban safety early warning and city development.
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Bossak, Brian H., and Mark R. Welford. "Spatio-Temporal Attributes of Pandemic and Epidemic Diseases." Geography Compass 4, no. 8 (August 2010): 1084–96. http://dx.doi.org/10.1111/j.1749-8198.2010.00355.x.
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Gao, Junyu, Tianzhu Zhang, and Changsheng Xu. "I Know the Relationships: Zero-Shot Action Recognition via Two-Stream Graph Convolutional Networks and Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8303–11. http://dx.doi.org/10.1609/aaai.v33i01.33018303.
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Recently, with the ever-growing action categories, zero-shot action recognition (ZSAR) has been achieved by automatically mining the underlying concepts (e.g., actions, attributes) in videos. However, most existing methods only exploit the visual cues of these concepts but ignore external knowledge information for modeling explicit relationships between them. In fact, humans have remarkable ability to transfer knowledge learned from familiar classes to recognize unfamiliar classes. To narrow the knowledge gap between existing methods and humans, we propose an end-to-end ZSAR framework based on a structured knowledge graph, which can jointly model the relationships between action-attribute, action-action, and attribute-attribute. To effectively leverage the knowledge graph, we design a novel Two-Stream Graph Convolutional Network (TS-GCN) consisting of a classifier branch and an instance branch. Specifically, the classifier branch takes the semantic-embedding vectors of all the concepts as input, then generates the classifiers for action categories. The instance branch maps the attribute embeddings and scores of each video instance into an attribute-feature space. Finally, the generated classifiers are evaluated on the attribute features of each video, and a classification loss is adopted for optimizing the whole network. In addition, a self-attention module is utilized to model the temporal information of videos. Extensive experimental results on three realistic action benchmarks Olympic Sports, HMDB51 and UCF101 demonstrate the favorable performance of our proposed framework.
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Li, Sihan, and Qi Li. "Revolutionary Strategy for Depicting Knowledge Graphs with Temporal Attributes." Mathematics 12, no. 9 (April 26, 2024): 1324. http://dx.doi.org/10.3390/math12091324.
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In practical applications, the temporal completeness of knowledge graphs is of great importance. However, previous studies have mostly focused on static knowledge graphs, generally neglecting the dynamic evolutionary properties of facts. Moreover, the unpredictable and limited availability of temporal knowledge graphs, together with the complex temporal dependency patterns, make current models inadequate for effectively describing facts that experience temporal transitions. To better represent the evolution of things over time, we provide a learning technique that uses quaternion rotation to describe temporal knowledge graphs. This technique describes the evolution of entities as a temporal rotation change in quaternion space. Compared to the Ermitian inner product in complex number space, the Hamiltonian product in quaternion space is better at showing how things might be connected. This leads to a learning process that is both more effective and more articulate. Experimental results demonstrate that our learning method significantly outperforms existing methods in capturing the dynamic evolution of temporal knowledge graphs, with improved accuracy and robustness across a range of benchmark datasets.
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Stepanov, Alexander. "The Correlation between Social and Artifact Meanings of Spatial Conject Nouns in Modern English." Izvestia of Smolensk State University, no. 1(57) (July 3, 2022): 101–14. http://dx.doi.org/10.35785/2072-9464-2022-57-1-101-114.
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The article is devoted to the research of modern English special conject
noun attribute system. Special attention is drawn to the special conjects nouns
with social meaning and their comparison to artifact conject nouns. The material
of this research is based on a sampling frame of 860 modern English conject
nouns with social and artifact meaning.
To study the selected group of nouns, Pearson’s correlation analysis is
used. The article has established presence or absence of semantic and formal at-
tributes from the list for the selected nouns. Correlation relationships between these attributes allow studying of semantic and formal peculiarities typical for two selected conject noun groups. According to the research hypothesis social
and artifact meanings of special conject nouns tend to have fundamental differences in the correlation relationships with formal and semantic attributes. The conject nouns with social meaning generally show a more intense correlation relationship system including relevant correlation coefficients with a broad spectrum of narrow social meanings and the most important word formation and temporal attributes.
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Tikhonova, E. S. "Ways to Specify Temporal Nouns in the “Livonian Rhymed Chronicle”." Discourse 5, no. 4 (October 29, 2019): 128–37. http://dx.doi.org/10.32603/2412-8562-2019-5-4-128-137.
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Introduction. Introduction explores synchronic and diachronic researches of temporal nouns in German as well as their classifications. Combinations of time adverbials with attributes of different kind in the Middle High German period are studied insufficiently. The research was conducted in the field of historical pragmatics, which allows to specify the speaker’s attitude to a statement.Methodology and sources. Material for the research is the “Livonian Rhymed Chronicle”. Provenience, specifics and the study of this text in linguistics and historiography as well as different points of view on the text’s poetics are considered. If the chronicle is considered as a conscious imitation of court epics, the functioning of its time adverbials is also to be considered from the court epic’s poetics’ point of view. Temporal organization of the Chronicle is not studied very well.Results and discussion. Results of the research represent examples of temporal nouns in the Chronicle. Collected examples are divided into groups according to by what part of speech is an attribute represented: by nouns, adjectives, pronouns, adverbs, numerals and subordinate clauses. Most characteristic examples for each group are analyzed. Discussion is devoted to summarizing the investigation and reviewing the results. A conclusion is drawn that temporal nouns due to their attributes receive some quality that bears the main semantic charge, whereas the temporal noun itself is partly non-semantic. Parallels to the usage of time adverbials in court epics are drawn.Conclusion. In the conclusion the connection between time presentation in the Chronicle and the medieval mentality is highlighted.
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Virgl, John A., W. James Rettie, and Daniel W. Coulton. "Spatial and temporal changes in seasonal range attributes in a declining barren-ground caribou herd." Rangifer 37, no. 1 (May 10, 2017): 31. http://dx.doi.org/10.7557/2.37.1.4115.
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From 1996 to 2015 the Bathurst caribou herd has declined from approximately 349,000 to 20,000 animals. Aboriginal traditional knowledge (TK) has recently observed the later arrival of the herd below the treeline, an attribute of the autumn range. Science also predicts that seasonal range attributes (e.g., area, location) likely vary with population size, and perhaps climate. We used Aboriginal TK and science to identify several seasonal range attributes that were examined for changes through time (decreasing population abundance). Attributes of seasonal ranges for female Bathurst caribou were calculated using satellite radio-collar data from January 1996 through October 2013. Climate data from CircumArctic Rangifer Monitoring and Assessment Network were analyzed for trends from 1979 to 2009. Analyses showed a significant decrease in the area of post-calving and autumn ranges, but no changes in winter and spring ranges. Results supported Aboriginal TK that female caribou have shifted the autumn range farther from the treeline and moved into the forest later in the year. Analysis of climate variables found no trends at the spatio-temporal scale of the post-calving to autumn ranges. Working hypotheses to explain these patterns, which are not mutually exclusive, include reduced predation risk, increased use of core areas at lower population density, and greater utilization of areas of taiga where arboreal and ground lichen availability and accessibility are relatively higher than in the forest. This analysis demonstrates how including Aboriginal TK can lead to stronger connections and results, with potential to provide new and different insights for further investigations.
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Ran, Yijun, Si-Yuan Liu, Xiaoyao Yu, Ke-Ke Shang, and Tao Jia. "Predicting future links with new nodes in temporal academic networks." Journal of Physics: Complexity 3, no. 1 (January 31, 2022): 015006. http://dx.doi.org/10.1088/2632-072x/ac4bee.
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Abstract Most real-world systems evolve over time in which entities and the interactions between entities are added and removed—new entities or relationships appear and old entities or relationships vanish. While most network evolutionary models can provide an iterative process for constructing global properties, they cannot capture the evolutionary mechanisms of real systems. Link prediction is hence proposed to predict future links which also can help us understand the evolution law of real systems. The aim of link prediction is to uncover missing links from known parts of the network or quantify the likelihood of the emergence of future links from current structures of the network. However, almost all existing studies ignored that old nodes tend to disappear and new nodes appear over time in real networks, especially in social networks. It is more challenging for link prediction since the new nodes do not have pre-existing structure information. To solve the temporal link prediction problems with new nodes, here we take into account nodal attribute similarity and the shortest path length, namely, ASSPL, to predict future links with new nodes. The results tested on scholar social network and academic funding networks show that it is highly effective and applicable for ASSPL in funding networks with time-evolving. Meanwhile, we make full use of an efficient parameter to exploit how network structure or nodal attribute has an impact on the performance of temporal link prediction. Finally, we find that nodal attributes and network structure complement each other well for predicting future links with new nodes in funding networks.
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Mummery, C. J., K. Patterson, J. R. Hodges, and C. J. Price. "Functional Neuroanatomy of the Semantic System: Divisible by What?" Journal of Cognitive Neuroscience 10, no. 6 (November 1998): 766–77. http://dx.doi.org/10.1162/089892998563059.
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Studies of patients with brain damage suggest that specific brain regions may be differentially involved in representing/processing certain categories of conceptual knowledge. With regard to the dissociation that has received the most attention—between the domains of living things and artifacts—a debate continues as to whether these category-specific effects reflect neural implementation of categories directly or some more basic properties of brain organization. The present positron emission tomography (PET) study addressed this issue by probing explicitly for differential activation associated with written names of objects from the domains of living things or artifacts during similarity judgments about different attributes of these objects. Subjects viewed triads of written object names and selected one of two response words as more similar to a target word according to a specified perceptual attribute (typical color of the objects) or an associative attribute (typical location of the objects). The control task required a similarity judgment about the number of syllables in the target and response words. All tasks were performed under two different stimulus conditions: names of living things and names of artifacts. Judgments for both domains and both attribute types activated an extensive, distributed, left-hemisphere semantic system, but showed some differential activation-particularly as a function of attribute type. The left temporooccipito-parietal junction showed enhanced activity for judgments about object location, whereas the left anteromedial temporal cortex and caudate nucleus were differentially activated by color judgments. Smaller differences were seen for living and nonliving domains, the positive findings being largely consistent with previous studies using objects; in particular, words denoting artifacts produced enhanced activation in the left posterior middle temporal gyrus. These results suggest that, within a distributed conceptual system activated by words, the more prominent neural distinction relates to type of attribute.
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Marzuqi, Ahmad, Kusuma Ayu Laksitowening, and Ibnu Asror. "Temporal Prediction on Students’ Graduation using Naïve Bayes and K-Nearest Neighbor Algorithm." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 2 (April 25, 2021): 682. http://dx.doi.org/10.30865/mib.v5i2.2919.
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Accreditation is a form of assessment of the feasibility and quality of higher education. One of the accreditation assessment factors is the percentage of graduation on time. A low percentage of on-time graduations can affect the assessment of accreditation of study programs. Predicting student graduation can be a solution to this problem. The prediction results can show that students are at risk of not graduating on time. Temporal prediction allows students and study programs to do the necessary treatment early. Prediction of graduation can use the learning analytics method, using a combination of the naïve bayes and the k-nearest neighbor algorithm. The Naïve Bayes algorithm looks for the courses that most influence graduation. The k-nearest neighbor algorithm as a classification method with the attribute limit used is 40% of the total attributes so that the algorithm becomes more effective and efficient. The dataset used is four batches of Telkom University Informatics Engineering student data involving data index of course scores 1, level 2, level 3, and level 4 data. The results obtained from this study are 5 attributes that most influence student graduation. As well as the results of the presentation of the combination naïve bayes and k-nearest neighbor algorithm with the largest percentage yield at level 1 75.40%, level 2 82.08%, level 3 81.91%, and level 4 90.42%.
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Lakshmanan, Valliappa, and Travis Smith. "Data Mining Storm Attributes from Spatial Grids." Journal of Atmospheric and Oceanic Technology 26, no. 11 (November 1, 2009): 2353–65. http://dx.doi.org/10.1175/2009jtecha1257.1.
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Abstract A technique to identify storms and capture scalar features within the geographic and temporal extent of the identified storms is described. The identification technique relies on clustering grid points in an observation field to find self-similar and spatially coherent clusters that meet the traditional understanding of what storms are. From these storms, geometric, spatial, and temporal features can be extracted. These scalar features can then be data mined to answer many types of research questions in an objective, data-driven manner. This is illustrated by using the technique to answer questions of forecaster skill and lightning predictability.
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Visalli, Michel, and Mara Virginia Galmarini. "Multi-attribute temporal descriptive methods in sensory analysis applied in food science: Protocol for a scoping review." PLOS ONE 17, no. 7 (July 26, 2022): e0270969. http://dx.doi.org/10.1371/journal.pone.0270969.
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Background Sensory perception is a temporal phenomenon highly present in food evaluation. Over the last decades, several sensory analysis methods have been developed to determine how our processing of the stimuli changes during tasting. These methods differ in several parameters: how attributes are characterized (intensity, dominance or applicability), the number of attributes evaluated, the moment of sample characterization (simultaneously with the tasting in continuous or discrete time, retrospectively), the required panel (trained subjects or consumers), etc. At the moment, there is no systematic review encompassing the full scope of this topic. This article presents the protocol for conducting a scoping review on multi-attribute temporal descriptive methods in sensory analysis in food science. Methods The protocol was developed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) extension for Scoping Reviews checklist. The research question was "how have multi-attribute temporal descriptive methods been implemented, used and compared in sensory analysis?". The eligibility criteria were defined using the PICOS (Population, Intervention, Comparator, Outcome, Study design) framework. This protocol details how the articles of the final review will be retrieved, selected and analyzed. The search will be based on the querying of two academic research databases (Scopus and Web of Science). The main topics reported in research involving sensory analyses methods will be identified and summarized in a data extraction form. This form (detailed in the protocol) will be used to report pertinent information regarding the objectives of the review. It could also be reused as a guideline for carrying out and reporting results of future research in a more standardized way. A quality appraisal process was derived from literature. It will be applied on the included articles of the review, and could also be re-used to ensure that future publications meet higher quality levels. Finally, for the sake of transparency, the limitations of the protocol are discussed.
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Tsogli, Vera, Sebastian Jentschke, and Stefan Koelsch. "Unpredictability of the “when” influences prediction error processing of the “what” and “where”." PLOS ONE 17, no. 2 (February 3, 2022): e0263373. http://dx.doi.org/10.1371/journal.pone.0263373.
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The capability to establish accurate predictions is an integral part of learning. Whether predictions about different dimensions of a stimulus interact with each other, and whether such an interaction affects learning, has remained elusive. We conducted a statistical learning study with EEG (electroencephalography), where a stream of consecutive sound triplets was presented with deviants that were either: (a) statistical, depending on the triplet ending probability, (b) physical, due to a change in sound location or (c) double deviants, i.e. a combination of the two. We manipulated the predictability of stimulus-onset by using random stimulus-onset asynchronies. Temporal unpredictability due to random onsets reduced the neurophysiological responses to statistical and location deviants, as indexed by the statistical mismatch negativity (sMMN) and the location MMN. Our results demonstrate that the predictability of one stimulus attribute influences the processing of prediction error signals of other stimulus attributes, and thus also learning of those attributes.
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Visentin, Marco, Gian Luca Marzocchi, and Alessandra Zammit. "L'interazione fra effetto olistico e rappresentazione temporale nella percezione delle marche." MERCATI & COMPETITIVITÀ, no. 1 (March 2011): 11–31. http://dx.doi.org/10.3280/mc2011-001002.
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L'obiettivo di questo studio č di analizzare l'interazione fra il pregiudizio olistico di marca e i livelli di rappresentazione degli oggetti percepiti. I nostri risultati documentano empiricamente che la semplice presentazione del compito di scelta fra marche con orizzonti temporali diversi non determina differenze significative nella percezione degli oggetti. Questo effetto si osserva sia per i giudizi basati su attributi che per quelli basati su distanze. Una volta che i giudizi lungo attributi vengono depurati dall'effetto olistico, la distanza temporale separa significativamente le rappresentazioni delle marche. Inoltre, gli effetti olistici stimati dalla procedura, mostrano una correlazione elevata con i giudizi empirici di preferenza, irrobustendo i risultati empirici presentati. Questo studio ha una rilevanza metodologica in quanto mostra l'interazione fra diversi livelli di rappresentazione e la presenza di pregiudizi olistici di marca.
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Azarnoush, Bahareh, Kamran Paynabar, Jennifer Bekki, and George Runger. "Monitoring Temporal Homogeneity in Attributed Network Streams." Journal of Quality Technology 48, no. 1 (January 2016): 28–43. http://dx.doi.org/10.1080/00224065.2016.11918149.
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CHEN, Changhong, Hehe DOU, and Zongliang GAN. "Collective Activity Recognition by Attribute-Based Spatio-Temporal Descriptor." IEICE Transactions on Information and Systems E98.D, no. 10 (2015): 1875–78. http://dx.doi.org/10.1587/transinf.2015edl8108.
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