Measuring Calves' Usage of Multiple Environmental Enrichment Objects Provided Simultaneously

This study aims to assess calf usage of five potential enrichment devices provided simultaneously. We used 25 weaned Holstein-Friesian calves housed in groups of five (five replicates), and their behavior was recorded continuously with video cameras. This longitudinal observational study used a pen equipped with a mechanical and fixed brush, cowhide, and horizontal and vertical ropes. Data collected included how many visits each object received per day, the type of object usage, and the duration of the visits. Calves used all five objects at least once, and they used items more during the daytime than at night. Brushes were used mainly for grooming (e.g., rubbing or scratching), while ropes and cowhide for oral interactions (e.g., licking, chewing, and biting), most likely to lack oral stimulations that would naturally be satisfied by suckling and grazing at this age. The objects most frequently used were the mechanical brush and the horizontal rope, and they received the highest number of visits (214.9 and 154.9 bouts/day, respectively). The least chosen object was the stationary brush, which had the lowest number of visits (62.9 bouts/day). The provision of multiple enrichment objects for weaned calves should be considered as they may add complexity and novelty to barren environments.

Visual Attention Patterns in Adults According to the Use of Things in Aided AAC VSDs: An Eye-tracking Study

Objectives: This study aims to investigate patterns of visual attention on a target object in VSDs (Visual Scene Displays) when they are designed with/without an action of usage of the object. We used eye-tracking technology to evaluate how the action of usage of an object in still photographs influenced the visual attention of adults without disabilities. We tried to examine visual attention on the contents of visual scene displays (VSDs).Methods: 25 college students participated in the study. Eye-tracking technology recorded point-of-gaze while participants viewed 20 photographs. Data from eye-tracking provided information on where participants were visually fixated and paid more attention on the presented VSDs including a target object.Results: Both total fixation duration and average fixation count were statistically significant. Participants visually fixated on the target object longer and more often when the object was being used in the presented VSDs. For AOI (Area Of Interest) time of the first fixation, after analyzing only a partial group that had the data match due to the difference in gaze pattern per subject, the average AOI time of the first fixation was shown to be faster when using an object in 6 out of 10 objects.Conclusion: This study supports the inclusion of an action of an object usage in VSDs suggesting that the act of object usage can partially influence the visual attention pattern of a user.

Graph-based service recommendation in Social Internet of Things

While it is well understood that the emerging Social Internet of Things offers the capability of effectively integrating and managing massive heterogeneous IoT objects, it also presents new challenges for suggesting useful objects with certain service for users due to complex relationships in Social Internet of Things, such as user’s object usage pattern and various social relationships among Social Internet of Things objects. In this study, we focus on the problem of service recommendation in Social Internet of Things, which is very important for many applications such as urban computing, smart cities, and health care. We propose a graph-based service recommendation framework by jointly considering social relationships of heterogeneous objects in Social Internet of Things and user’s preferences. More exactly, we learn user’s preference from his or her object usage events with a latent variable model. Then, we model users, objects, and their relationships with a knowledge graph and regard Social Internet of Things service recommendation as a knowledge graph completion problem, where the “like” property that connects users to services needs to be predicted. To demonstrate the utility of the proposed model, we have built a Social Internet of Things testbed to validate our approach and the experimental results demonstrate its feasibility and effectiveness.

Exploiting Patterns of Object Use for Human Activity Recognition

The accurate recognition of activities of daily living (ADL) is fundamental in the support and provision of assistance for the elderly and cognitively impaired. Current ontology-based techniques and knowledge-driven model object concepts form assumptions and everyday knowledge of objects used for activities. Activities modelled from assumptions and everyday knowledge can lead to incorrect recognition results of routine activities and possible failure to detect abnormal activity trends. A significant step to the accurate recognition of activities of daily living is the discovery of the object usage for specific routine activities. This chapter presents an approach that discovers object usage for routine activities using latent Dirichlet allocation (LDA) topic modelling. The object usage discovery augments an activity ontology that enables recognition of simple activities of daily living in the home environment. The proposed approach is evaluated and validated using the Kasteren and Ordonez datasets.

Semantic and Pragmatic Parameters of Quasi-Symbols Description (Based on the Example of the Cultural Fetish Code)

The article considers one of the units forming the linguistic and cultural level ‒ a quasi-symbol, which denotes ideas in an imperative form. The differential feature of the quasi-symbol is the fact that it encourages certain actions; it models the interpretant’s behavior. This unit is characterized by vivid imagery and it may include an evaluative and emotive component in its meaning. The semantics of the quasi-symbol is characterized by multiple layers. The article discusses and tests the parameters of lexicography of the quasi-symbol using the example of units characterizing the cultural fetish code. The main research method is linguistic and cultural interpretation, which consists in interpreting the semantics of a unit, characterizing its pragmatic properties in cultural categories. The study finds that the names of symbols and quasi-symbols can coincide formally. The semantics of the quasisymbol may differ significantly from the meaning of the correlating symbol. It is shown that the functional specificity of the name acting as a cultural symbol is unambiguous: a unit performs one cultural function. The basis for forming a symbolic meaning is the observation of the object usage and its properties. The article proves that the quasi-symbol can have a more complex semantic structure in comparison with the correlating symbol represented by the object. The main intentions of quasi-symbols include a statement of the situation, assessment, and warning. The paper determines the parameters sufficient for lexicography of the quasi-symbol such as significative content, an interpretant, the basis for the formation of a symbolic meaning, intentionality and features of functioning.

Analysis and application of martial arts video image based on fuzzy clustering algorithm

The intelligent scheduling algorithm for hierarchical data migration is a key issue in data management. Mass media content platforms and the discovery of content object usage patterns is the basic schedule of data migration. We add QPop, the dimensionality reduction result of media content usage logs, as content objects for discovering usage patterns. On this basis, a clustering algorithm QPop is proposed to increase the time segmentation, thereby improving the mining performance. We hired the standard C-means algorithm as the clustering core and used segmentation to conduct an experimental mining process to collect the ted QPop increments in practical applications. The results show that the improved algorithm has good robustness in cluster cohesion and other indicators, slightly better than the basic model.

An improved algorithm for mining media content application patterns based on QPop increasing disk time domain segmentation and upgrading1

The intelligent scheduling algorithm for hierarchical data migration is a key issue in data management. Mass media content platforms and the discovery of content object usage patterns is the basic schedule of data migration. We add QPop, the dimensionality reduction result of media content usage logs, as content objects for discovering usage patterns. On this basis, a clustering algorithm QPop is proposed to increase the time segmentation, thereby improving the mining performance. We hired the standard C-means algorithm as the clustering core and used segmentation to conduct an experimental mining process to collect the ted QPop increments in practical applications. The results show that the improved algorithm has good robustness in cluster cohesion and other indicators, slightly better than the basic model.