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In an efficient and flexible human-robot collaborative work environment, a robot team member must be able to recognize both explicit requests and implied actions from human users. Identifying “what to do” in such cases requires an agent to have the ability to construct associations between objects, their actions, and the effect of actions on the environment. In this regard, semantic memory is being introduced to understand the explicit cues and their relationships with available objects and required skills to make “tea” and “sandwich”. We have extended our previous hierarchical robot control architecture to add the capability to execute the most appropriate task based on both feedback from the user and the environmental context. To validate this system, two types of skills were implemented in the hierarchical task tree: 1) Tea making skills and 2) Sandwich making skills. During the conversation between the robot and the human, the robot was able to determine the hidden context using ontology and began to act accordingly. For instance, if the person says “I am thirsty” or “It is cold outside” the robot will start to perform the tea-making skill. In contrast, if the person says, “I am hungry” or “I need something to eat”, the robot will make the sandwich. A humanoid robot Baxter was used for this experiment. We tested three scenarios with objects at different positions on the table for each skill. We observed that in all cases, the robot used only objects that were relevant to the skill. 

An abundance of biomedical data is generated in the form of clinical notes, reports, and research articles available online. This data holds valuable information that requires extraction, retrieval, and transformation into actionable knowledge. However, this information has various access challenges due to the need for precise machine-interpretable semantic metadata required by search engines. Despite search engines' efforts to interpret the semantics information, they still struggle to index, search, and retrieve relevant information accurately. To address these challenges, we propose a novel graph-based semantic knowledge-sharing approach to enhance the quality of biomedical semantic annotation by engaging biomedical domain experts. In this approach, entities in the knowledge-sharing environment are interlinked and play critical roles. Authorial queries can be posted on the "Knowledge Cafe," and community experts can provide recommendations for semantic annotations. The community can further validate and evaluate the expert responses through a voting scheme resulting in a transformed "Knowledge Cafe" environment that functions as a knowledge graph with semantically linked entities. We evaluated the proposed approach through a series of scenarios, resulting in precision, recall, F1-score, and accuracy assessment matrices. Our results showed an acceptable level of accuracy at approximately 90%. The source code for "Semantically" is freely available at: https://github.com/bukharilab/Semantically 

There are huge on-going challenges to timely access of accurate online biomedical content due to exponential growth of unstructured biomedical data. Therefore, semantic annotations are essentially required with the biomedical content in order to improve search engines’ context-aware indexing, search efficiency, and precision of the retrieved results. In this study, we propose a personalized semantic annotation recommendations approach to biomedical content through an expanded socio-technical approach. Our layered architecture generates annotations on the users’ entered text in the first layer. To optimize the yielded annotations, users can seek help from professional experts by posing specific questions to them. The socio-technical system also connects help seekers (users) to help providers (experts) employing the pre-trained BERT embedding, which matches the profile similarity scores of users and experts at various levels and suggests a run-time compatible match (of the help seeker and the help provider). Our approach overcomes previous systems’ limitations as they are predominantly non-collaborative and laborious. While performing experiments, we analyzed the performance enhancements offered by our socio-technical approach in improving the semantic annotations in three scenarios in various contexts. Our results show overall achievement of 89.98% precision, 89.61% recall, and an 89.45% f1-score at the system level. Comparatively speaking, a high accuracy of 90% was achieved with the socio-technical approach whereas the traditional approach could only reach 87% accuracy. Our novel socio-technical approach produces apt annotation recommendations that would definitely be helpful for various secondary uses ranging from context-aware indexing to retrieval accuracy improvements. 

Each day a vast amount of unstructured content is generated in the biomedical domain from various sources such as clinical notes, research articles and medical reports. Such content contain a sufficient amount of efficient and meaningful information that needs to be converted into actionable knowledge for secondary use. However, accessing precise biomedical content is quite challenging because of content heterogeneity, missing and imprecise metadata and unavailability of associated semantic tags required for search engine optimization. We have introduced a socio-technical semantic annotation optimization approach that enhance the semantic search of biomedical contents. The proposed approach consist of layered architecture. At First layer (Preliminary Semantic Enrichment), it annotates the biomedical contents with the ontological concepts from NCBO BioPortal. With the growing biomedical information, the suggested semantic annotations from NCBO Bioportal are not always correct. Therefore, in the second layer (Optimizing the Enriched Semantic Information), we introduce a knowledge sharing scheme through which authors/users could request for recommendations from other users to optimize the semantic enrichment process. To guage the credibility of the the human recommended, our systems records the recommender confidence score, collects community voting against previous recommendations, stores percentage of correctly suggested annotation and translates that into an index to later connect right users to get suggestions to optimize the semantic enrichment of biomedical contents. At the preliminary layer of annotation from NCBO, we analyzed the n-gram strategy for biomedical word boundary identification. We have found that NCBO recognizes biomedical terms for n-gram-1 more than for n-gram-2 to n-gram-5. Similarly, a statistical measure conducted on significant features using the Wilson score and data normalization. In contrast, the proposed methodology achieves an suitable accuracy of ≈90% for the semantic optimization approach. 

Neuronal networks are the standard heuristic model today for describing brain activity associated with animal behavior. Recent studies have revealed an extensive role for a completely distinct layer of networked activities in the brain—the gene regulatory network (GRN)—that orchestrates expression levels of hundreds to thousands of genes in a behavior-related manner. We examine emerging insights into the relationships between these two types of networks and discuss their interplay in spatial as well as temporal dimensions, across multiple scales of organization. We discuss properties expected of behavior-related GRNs by drawing inspiration from the rich literature on GRNs related to animal development, comparing and contrasting these two broad classes of GRNs as they relate to their respective phenotypic manifestations. Developmental GRNs also represent a third layer of network biology, playing out over a third timescale, which is believed to play a crucial mediatory role between neuronal networks and behavioral GRNs. We end with a special emphasis on social behavior, discuss whether unique GRN organization andcis-regulatory architecture underlies this special class of behavior, and review literature that suggests an affirmative answer.